{
 "cells": [
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-06T02:17:45.068111Z",
     "start_time": "2025-06-06T02:17:37.605817Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import os\n",
    "\n",
    "def read_files_from_folder(folder_path):\n",
    "    \"\"\"\n",
    "    读取指定文件夹中的所有文件内容\n",
    "    \"\"\"\n",
    "    all_files = []\n",
    "    for root, dirs, files in os.walk(folder_path):\n",
    "        for file in files:\n",
    "            if file.endswith('.txt'):  # 确保只读取文本文件\n",
    "                file_path = os.path.join(root, file)\n",
    "                with open(file_path, 'r', encoding='utf-8') as f:\n",
    "                    all_files.append(f.read())\n",
    "    return all_files\n",
    "\n",
    "# 设置文件夹路径\n",
    "neg_folder_path = r'E:\\Ruanjian\\Python\\pycharm\\Ren\\Ren\\机器学习\\aclImdb\\test\\neg'\n",
    "pos_folder_path = r'E:\\Ruanjian\\Python\\pycharm\\Ren\\Ren\\机器学习\\aclImdb\\test\\pos'\n",
    "\n",
    "# 读取文件夹中的所有文件\n",
    "neg_files = read_files_from_folder(neg_folder_path)\n",
    "pos_files = read_files_from_folder(pos_folder_path)\n",
    "\n",
    "# 打印读取的文件数量\n",
    "print(f\"负面评论文件数量：{len(neg_files)}\")\n",
    "print(f\"正面评论文件数量：{len(pos_files)}\")"
   ],
   "id": "2839f8649c2dbbd2",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "负面评论文件数量：12500\n",
      "正面评论文件数量：12500\n"
     ]
    }
   ],
   "execution_count": 32
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-06T02:17:54.929828Z",
     "start_time": "2025-06-06T02:17:47.565908Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import os\n",
    "\n",
    "def read_files_from_folder(folder_path):\n",
    "    \"\"\"\n",
    "    读取指定文件夹中的所有文件内容\n",
    "    \"\"\"\n",
    "    all_files = []\n",
    "    for root, dirs, files in os.walk(folder_path):\n",
    "        for file in files:\n",
    "            if file.endswith('.txt'):  # 确保只读取文本文件\n",
    "                file_path = os.path.join(root, file)\n",
    "                with open(file_path, 'r', encoding='utf-8') as f:\n",
    "                    all_files.append(f.read())\n",
    "    return all_files\n",
    "\n",
    "def write_to_file(file_path, content):\n",
    "    \"\"\"\n",
    "    将内容写入文件\n",
    "    \"\"\"\n",
    "    with open(file_path, 'w', encoding='utf-8') as file:\n",
    "        file.write(content)\n",
    "\n",
    "# 设置文件夹路径\n",
    "neg_folder_path = r'E:\\Ruanjian\\Python\\pycharm\\Ren\\Ren\\机器学习\\aclImdb\\test\\neg'\n",
    "pos_folder_path = r'E:\\Ruanjian\\Python\\pycharm\\Ren\\Ren\\机器学习\\aclImdb\\test\\pos'\n",
    "output_file_path = r'E:\\Ruanjian\\Python\\pycharm\\Ren\\Ren\\机器学习\\aclImdb\\test\\combined_reviews.txt'\n",
    "\n",
    "# 读取文件夹中的所有文件\n",
    "neg_files = read_files_from_folder(neg_folder_path)\n",
    "pos_files = read_files_from_folder(pos_folder_path)\n",
    "\n",
    "# 合并所有文件内容\n",
    "all_content = '\\n'.join(neg_files + pos_files)\n",
    "\n",
    "# 将合并后的内容写入新文件\n",
    "write_to_file(output_file_path, all_content)\n",
    "\n",
    "print(f\"所有评论已合并到文件：{output_file_path}\")"
   ],
   "id": "57427f9fdccf48c7",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "所有评论已合并到文件：E:\\Ruanjian\\Python\\pycharm\\Ren\\Ren\\机器学习\\aclImdb\\test\\combined_reviews.txt\n"
     ]
    }
   ],
   "execution_count": 33
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-06T02:18:12.982838Z",
     "start_time": "2025-06-06T02:18:12.976334Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import os\n",
    "\n",
    "# 设置目录路径\n",
    "directory_path = r'E:\\Ruanjian\\Python\\pycharm\\Ren\\Ren\\机器学习\\aclImdb\\test'\n",
    "\n",
    "# 列出目录中的所有文件\n",
    "files_in_directory = os.listdir(directory_path)\n",
    "\n",
    "# 打印文件列表\n",
    "print(\"目录中的文件：\")\n",
    "for file in files_in_directory:\n",
    "    print(file)\n",
    "\n",
    "# 检查特定文件是否存在\n",
    "if 'combined_reviews.txt' in files_in_directory:\n",
    "    print(\"\\n文件 'combined_reviews.txt' 已成功保存在目录中。\")\n",
    "else:\n",
    "    print(\"\\n文件 'combined_reviews.txt' 未找到，请检查保存路径。\")"
   ],
   "id": "8bd11111783fc9f2",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "目录中的文件：\n",
      "combined_reviews.txt\n",
      "combined_reviews_lower.txt\n",
      "labeledBow.feat\n",
      "neg\n",
      "pos\n",
      "urls_neg.txt\n",
      "urls_pos.txt\n",
      "\n",
      "文件 'combined_reviews.txt' 已成功保存在目录中。\n"
     ]
    }
   ],
   "execution_count": 34
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-06T02:18:40.483404Z",
     "start_time": "2025-06-06T02:18:39.007779Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 将合并后的评论文本转为小写\n",
    "with open(output_file_path, 'r', encoding='utf-8') as file:\n",
    "    content = file.read()\n",
    "\n",
    "lower_content = content.lower()\n",
    "\n",
    "# 可选择将小写内容写回文件或保存为新文件\n",
    "lower_output_file_path = r'E:\\Ruanjian\\Python\\pycharm\\Ren\\Ren\\机器学习\\aclImdb\\test\\combined_reviews_lower.txt'\n",
    "write_to_file(lower_output_file_path, lower_content)\n",
    "\n",
    "print(f\"所有评论已转为小写并保存到文件：{lower_output_file_path}\")"
   ],
   "id": "3f88497745ac764e",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "所有评论已转为小写并保存到文件：E:\\Ruanjian\\Python\\pycharm\\Ren\\Ren\\机器学习\\aclImdb\\test\\combined_reviews_lower.txt\n"
     ]
    }
   ],
   "execution_count": 36
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-06T02:19:24.287794Z",
     "start_time": "2025-06-06T02:18:47.959709Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize\n",
    "import nltk\n",
    "\n",
    "# 下载缺失的 punkt_tab 资源\n",
    "nltk.download('punkt_tab')\n",
    "\n",
    "# 如果依然报错，可以尝试重新下载 punkt\n",
    "#nltk.download('punkt')\n",
    "# 下载所需的nltk资源（如未下载过）\n",
    "nltk.download('punkt')\n",
    "nltk.download('stopwords')\n",
    "\n",
    "# 读取小写化后的评论内容\n",
    "with open(lower_output_file_path, 'r', encoding='utf-8') as file:\n",
    "    text = file.read()\n",
    "\n",
    "# 分词\n",
    "tokens = word_tokenize(text)\n",
    "\n",
    "# 获取英文停用词列表\n",
    "stop_words = set(stopwords.words('english'))\n",
    "\n",
    "# 去除停用词\n",
    "filtered_tokens = [word for word in tokens if word.isalpha() and word not in stop_words]\n",
    "\n",
    "print(f\"分词后去除停用词的前100个词：\\n{filtered_tokens[:100]}\")"
   ],
   "id": "57b5e36043d15709",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt_tab to\n",
      "[nltk_data]     C:\\Users\\任老大\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt_tab is already up-to-date!\n",
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\任老大\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\任老大\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "分词后去除停用词的前100个词：\n",
      "['costner', 'dragged', 'movie', 'far', 'longer', 'necessary', 'aside', 'terrific', 'sea', 'rescue', 'sequences', 'care', 'characters', 'us', 'ghosts', 'closet', 'costner', 'character', 'realized', 'early', 'forgotten', 'much', 'later', 'time', 'care', 'character', 'really', 'care', 'cocky', 'overconfident', 'ashton', 'kutcher', 'problem', 'comes', 'kid', 'thinks', 'better', 'anyone', 'else', 'around', 'shows', 'signs', 'cluttered', 'closet', 'obstacle', 'appears', 'winning', 'costner', 'finally', 'well', 'past', 'half', 'way', 'point', 'stinker', 'costner', 'tells', 'us', 'kutcher', 'ghosts', 'told', 'kutcher', 'driven', 'best', 'prior', 'inkling', 'foreshadowing', 'magic', 'could', 'keep', 'turning', 'hour', 'example', 'majority', 'action', 'films', 'generic', 'boring', 'really', 'nothing', 'worth', 'watching', 'complete', 'waste', 'talents', 'ice', 'cube', 'proven', 'many', 'times', 'capable', 'acting', 'acting', 'well', 'bother', 'one', 'go', 'see', 'new', 'jack']\n"
     ]
    }
   ],
   "execution_count": 37
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-06T02:19:28.844378Z",
     "start_time": "2025-06-06T02:19:28.792280Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 检查 labels 是否正确定义且为列表或数组\n",
    "# labels 应该是与 reviews 数量一致的标签列表，例如：\n",
    "# labels = [0, 1, 0, 1, ...]  # 0 表示负面，1 表示正面\n",
    "\n",
    "# 如果你还没有定义 labels，请根据你的数据集生成它\n",
    "# 例如，如果 neg_files 和 pos_files 分别为负面和正面评论列表：\n",
    "labels = [0] * len(neg_files) + [1] * len(pos_files)\n",
    "\n",
    "# 重新合并评论和标签，确保一一对应\n",
    "reviews = neg_files + pos_files\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# 划分训练集和测试集\n",
    "train_reviews, test_reviews, train_labels, test_labels = train_test_split(\n",
    "    reviews, labels, test_size=0.2, random_state=42, stratify=labels\n",
    ")\n",
    "\n",
    "print(f\"训练集数量: {len(train_reviews)}\")\n",
    "print(f\"测试集数量: {len(test_reviews)}\")"
   ],
   "id": "db060b88de733644",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集数量: 20000\n",
      "测试集数量: 5000\n"
     ]
    }
   ],
   "execution_count": 38
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-06T02:19:33.715604Z",
     "start_time": "2025-06-06T02:19:32.426222Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from collections import Counter\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 假设你有训练集和测试集的标签列表 train_labels 和 test_labels\n",
    "# 这里需要你根据实际情况替换变量名或加载标签数据\n",
    "\n",
    "# 示例：如果标签是 0（负面）和 1（正面）\n",
    "# train_labels = [...]\n",
    "# test_labels = [...]\n",
    "\n",
    "\n",
    "# 统计类别分布\n",
    "train_counter = Counter(train_labels)\n",
    "test_counter = Counter(test_labels)\n",
    "\n",
    "# 绘制柱状图\n",
    "fig, axes = plt.subplots(1, 2, figsize=(10, 4))\n",
    "\n",
    "axes[0].bar(train_counter.keys(), train_counter.values(), color=['skyblue', 'salmon'])\n",
    "axes[0].set_title('Train Set Class Distribution')\n",
    "axes[0].set_xlabel('Class')\n",
    "axes[0].set_ylabel('Count')\n",
    "\n",
    "axes[1].bar(test_counter.keys(), test_counter.values(), color=['skyblue', 'salmon'])\n",
    "axes[1].set_title('Test Set Class Distribution')\n",
    "axes[1].set_xlabel('Class')\n",
    "axes[1].set_ylabel('Count')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "id": "f6de1ba5d9667b33",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 39
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-06T02:20:25.699221Z",
     "start_time": "2025-06-06T02:20:21.021848Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 统计词频\n",
    "word_freq = Counter(filtered_tokens)\n",
    "top_10_words = word_freq.most_common(10)\n",
    "\n",
    "# 绘制Top-10高频词汇柱状图\n",
    "words, counts = zip(*top_10_words)\n",
    "plt.figure(figsize=(8, 4))\n",
    "plt.bar(words, counts, color='skyblue')\n",
    "plt.title('Top-10 high')\n",
    "plt.xlabel('词汇')\n",
    "plt.ylabel('频率')\n",
    "plt.show()\n",
    "\n",
    "# 统计字符频率\n",
    "char_freq = Counter(''.join(filtered_tokens))\n",
    "top_10_chars = char_freq.most_common(10)\n",
    "\n",
    "# 绘制Top-10高频字符柱状图\n",
    "chars, char_counts = zip(*top_10_chars)\n",
    "plt.figure(figsize=(8, 4))\n",
    "plt.bar(chars, char_counts, color='salmon')\n",
    "plt.title('Top-10 high')\n",
    "plt.xlabel('字符')\n",
    "plt.ylabel('频率')\n",
    "plt.show()"
   ],
   "id": "8b5bdc79e24ac902",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Ruanjian\\Python\\anacondn\\envs\\jinke\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 39057 (\\N{CJK UNIFIED IDEOGRAPH-9891}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "E:\\Ruanjian\\Python\\anacondn\\envs\\jinke\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 29575 (\\N{CJK UNIFIED IDEOGRAPH-7387}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "E:\\Ruanjian\\Python\\anacondn\\envs\\jinke\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 35789 (\\N{CJK UNIFIED IDEOGRAPH-8BCD}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "E:\\Ruanjian\\Python\\anacondn\\envs\\jinke\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 27719 (\\N{CJK UNIFIED IDEOGRAPH-6C47}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Ruanjian\\Python\\anacondn\\envs\\jinke\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23383 (\\N{CJK UNIFIED IDEOGRAPH-5B57}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "E:\\Ruanjian\\Python\\anacondn\\envs\\jinke\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31526 (\\N{CJK UNIFIED IDEOGRAPH-7B26}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 41
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-06T02:20:36.194171Z",
     "start_time": "2025-06-06T02:20:30.286548Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import requests\n",
    "import os\n",
    "\n",
    "# 字体文件的下载链接\n",
    "font_url = 'https://noto-website-2.storage.googleapis.com/pkgs/NotoSansCJKsc-Regular.otf'\n",
    "\n",
    "# 字体文件保存的本地路径\n",
    "font_path = 'NotoSansCJKsc-Regular.otf'\n",
    "\n",
    "# 发送HTTP GET请求下载字体文件\n",
    "response = requests.get(font_url)\n",
    "\n",
    "# 检查请求是否成功\n",
    "if response.status_code == 200:\n",
    "    # 写入文件\n",
    "    with open(font_path, 'wb') as f:\n",
    "        f.write(response.content)\n",
    "    print(\"字体文件下载成功，保存在：\", font_path)\n",
    "else:\n",
    "    print(\"下载失败，状态码：\", response.status_code)"
   ],
   "id": "ff3bdb88370d134f",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "下载失败，状态码： 403\n"
     ]
    }
   ],
   "execution_count": 42
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-06T02:20:58.781744Z",
     "start_time": "2025-06-06T02:20:41.915270Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "# 以IMDb为例，使用5000维词汇级特征\n",
    "tfidf_vectorizer_imdb = TfidfVectorizer(max_features=5000, tokenizer=word_tokenize)\n",
    "imdb_tfidf_matrix = tfidf_vectorizer_imdb.fit_transform([' '.join(filtered_tokens)])\n",
    "\n",
    "print(f\"IMDb TF-IDF 特征矩阵形状: {imdb_tfidf_matrix.shape}\")\n",
    "\n",
    "# 如果是THUCNews，使用字符级输入和3000维特征\n",
    "tfidf_vectorizer_thucnews = TfidfVectorizer(max_features=3000, analyzer='char')\n",
    "thucnews_tfidf_matrix = tfidf_vectorizer_thucnews.fit_transform([''.join(filtered_tokens)])\n",
    "\n",
    "print(f\"THUCNews TF-IDF 特征矩阵形状: {thucnews_tfidf_matrix.shape}\")"
   ],
   "id": "23b15e68bbfedf8a",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Ruanjian\\Python\\anacondn\\envs\\jinke\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:517: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IMDb TF-IDF 特征矩阵形状: (1, 5000)\n",
      "THUCNews TF-IDF 特征矩阵形状: (1, 68)\n"
     ]
    }
   ],
   "execution_count": 43
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-06T02:21:05.037135Z",
     "start_time": "2025-06-06T02:21:04.940981Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "# 对IMDb的TF-IDF特征进行PCA降维，保留95%方差\n",
    "pca_imdb = PCA(n_components=0.95, svd_solver='full')\n",
    "imdb_tfidf_reduced = pca_imdb.fit_transform(imdb_tfidf_matrix.toarray())\n",
    "print(f\"IMDb TF-IDF 降维后形状: {imdb_tfidf_reduced.shape}\")\n",
    "\n",
    "# 对THUCNews的TF-IDF特征进行PCA降维，保留95%方差\n",
    "pca_thucnews = PCA(n_components=0.95, svd_solver='full')\n",
    "thucnews_tfidf_reduced = pca_thucnews.fit_transform(thucnews_tfidf_matrix.toarray())\n",
    "print(f\"THUCNews TF-IDF 降维后形状: {thucnews_tfidf_reduced.shape}\")\n",
    "\n",
    "# 词嵌入部分可使用如Word2Vec、GloVe等方法，具体实现可根据需求补充"
   ],
   "id": "d7b6e2abf9448ecf",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IMDb TF-IDF 降维后形状: (1, 1)\n",
      "THUCNews TF-IDF 降维后形状: (1, 1)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Ruanjian\\Python\\anacondn\\envs\\jinke\\Lib\\site-packages\\sklearn\\decomposition\\_pca.py:586: RuntimeWarning: invalid value encountered in divide\n",
      "  explained_variance_ = (S**2) / (n_samples - 1)\n",
      "E:\\Ruanjian\\Python\\anacondn\\envs\\jinke\\Lib\\site-packages\\sklearn\\decomposition\\_pca.py:586: RuntimeWarning: invalid value encountered in divide\n",
      "  explained_variance_ = (S**2) / (n_samples - 1)\n"
     ]
    }
   ],
   "execution_count": 44
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-06T02:23:57.196162Z",
     "start_time": "2025-06-06T02:21:49.862580Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import pandas as pd\n",
    "import sklearn\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "from sklearn.pipeline import Pipeline\n",
    "import time\n",
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "# 模型评估函数\n",
    "def evaluate_model(model, X_train, y_train, X_test, y_test, model_name):\n",
    "    \"\"\"训练并评估单个模型\"\"\"\n",
    "    print(f\"\\n正在训练 {model_name} 模型...\")\n",
    "    start_time = time.time()\n",
    "    \n",
    "    # 创建pipeline\n",
    "    pipeline = Pipeline([\n",
    "        ('tfidf', TfidfVectorizer(max_features=10000, stop_words='english')),\n",
    "        ('clf', model)\n",
    "    ])\n",
    "    \n",
    "    # 训练模型\n",
    "    pipeline.fit(X_train, y_train)\n",
    "    \n",
    "    # 预测\n",
    "    y_pred = pipeline.predict(X_test)\n",
    "    y_pred_prob = pipeline.predict_proba(X_test)[:, 1] if hasattr(pipeline, \"predict_proba\") else None\n",
    "    \n",
    "    # 评估\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    precision = precision_score(y_test, y_pred, average='weighted')\n",
    "    recall = recall_score(y_test, y_pred, average='weighted')\n",
    "    f1 = f1_score(y_test, y_pred, average='weighted')\n",
    "    train_time = time.time() - start_time\n",
    "    \n",
    "    print(f\"{model_name} 训练时间: {train_time:.2f}秒\")\n",
    "    print(f\"{model_name} 分类报告:\")\n",
    "    print(classification_report(y_test, y_pred))\n",
    "    \n",
    "    return {\n",
    "        'Model': model_name,\n",
    "        'Train Time(s)': round(train_time, 2),\n",
    "        'Accuracy': accuracy,\n",
    "        'Precision': precision,\n",
    "        'Recall': recall,\n",
    "        'F1-Score': f1\n",
    "    }, y_pred, y_pred_prob\n",
    "\n",
    "\n",
    "# 检测sklearn版本并使用兼容的CalibratedClassifierCV参数\n",
    "sklearn_version = sklearn.__version__\n",
    "if int(sklearn_version.split('.')[1]) >= 24:  # 0.24+版本\n",
    "    base_estimator_param = 'base_estimator'\n",
    "else:  # 0.23及以下版本\n",
    "    base_estimator_param = 'estimator'\n",
    "\n",
    "# 使用CalibratedClassifierCV获取概率估计，兼容不同sklearn版本\n",
    "svm_model = CalibratedClassifierCV(\n",
    "    **{base_estimator_param: LinearSVC(C=1.0, random_state=42)},\n",
    "    cv=3\n",
    ")\n",
    "\n",
    "# 定义要评估的模型\n",
    "models = [\n",
    "    (\"Logistic Regression\", LogisticRegression(max_iter=1000)),\n",
    "    (\"SVM (Probabilistic)\", svm_model),\n",
    "    (\"Decision Tree\", DecisionTreeClassifier()),\n",
    "    (\"Random Forest\", RandomForestClassifier()),\n",
    "    (\"KNN\", KNeighborsClassifier()),\n",
    "    (\"Naive Bayes\", MultinomialNB())\n",
    "]\n",
    "\n",
    "# 存储结果的DataFrame\n",
    "results = pd.DataFrame(columns=['Model', 'Train Time(s)', 'Accuracy', 'Precision', 'Recall', 'F1-Score'])\n",
    "\n",
    "# 存储预测结果和概率\n",
    "predictions = {}\n",
    "pred_probs = {}\n",
    "\n",
    "# 训练并评估每个模型\n",
    "for name, model in models:\n",
    "    model_result, y_pred, y_pred_prob = evaluate_model(\n",
    "        model, train_reviews, train_labels, test_reviews, test_labels, name\n",
    "    )\n",
    "    results.loc[len(results)] = model_result\n",
    "    predictions[name] = y_pred\n",
    "    pred_probs[name] = y_pred_prob"
   ],
   "id": "851f1277cf3247f9",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "正在训练 Logistic Regression 模型...\n",
      "Logistic Regression 训练时间: 7.99秒\n",
      "Logistic Regression 分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.89      0.90      2500\n",
      "           1       0.89      0.91      0.90      2500\n",
      "\n",
      "    accuracy                           0.90      5000\n",
      "   macro avg       0.90      0.90      0.90      5000\n",
      "weighted avg       0.90      0.90      0.90      5000\n",
      "\n",
      "\n",
      "正在训练 SVM (Probabilistic) 模型...\n",
      "SVM (Probabilistic) 训练时间: 6.39秒\n",
      "SVM (Probabilistic) 分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.89      0.89      2500\n",
      "           1       0.89      0.90      0.90      2500\n",
      "\n",
      "    accuracy                           0.90      5000\n",
      "   macro avg       0.90      0.90      0.90      5000\n",
      "weighted avg       0.90      0.90      0.90      5000\n",
      "\n",
      "\n",
      "正在训练 Decision Tree 模型...\n",
      "Decision Tree 训练时间: 30.66秒\n",
      "Decision Tree 分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.72      0.73      0.72      2500\n",
      "           1       0.72      0.71      0.72      2500\n",
      "\n",
      "    accuracy                           0.72      5000\n",
      "   macro avg       0.72      0.72      0.72      5000\n",
      "weighted avg       0.72      0.72      0.72      5000\n",
      "\n",
      "\n",
      "正在训练 Random Forest 模型...\n",
      "Random Forest 训练时间: 56.16秒\n",
      "Random Forest 分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.87      0.86      2500\n",
      "           1       0.87      0.84      0.85      2500\n",
      "\n",
      "    accuracy                           0.86      5000\n",
      "   macro avg       0.86      0.86      0.86      5000\n",
      "weighted avg       0.86      0.86      0.86      5000\n",
      "\n",
      "\n",
      "正在训练 KNN 模型...\n",
      "KNN 训练时间: 20.49秒\n",
      "KNN 分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.74      0.77      2500\n",
      "           1       0.76      0.81      0.78      2500\n",
      "\n",
      "    accuracy                           0.78      5000\n",
      "   macro avg       0.78      0.78      0.78      5000\n",
      "weighted avg       0.78      0.78      0.78      5000\n",
      "\n",
      "\n",
      "正在训练 Naive Bayes 模型...\n",
      "Naive Bayes 训练时间: 5.30秒\n",
      "Naive Bayes 分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.89      0.88      2500\n",
      "           1       0.89      0.87      0.88      2500\n",
      "\n",
      "    accuracy                           0.88      5000\n",
      "   macro avg       0.88      0.88      0.88      5000\n",
      "weighted avg       0.88      0.88      0.88      5000\n",
      "\n"
     ]
    }
   ],
   "execution_count": 47
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-06T02:24:02.871902Z",
     "start_time": "2025-06-06T02:24:01.501552Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# 确保中文字体正确显示（添加字体配置）\n",
    "plt.rcParams[\"font.family\"] = [\"SimHei\", \"Microsoft YaHei\"]  # 使用系统存在的中文字体\n",
    "plt.rcParams[\"axes.unicode_minus\"] = False  # 解决负号显示问题\n",
    "\n",
    "def plot_confusion_matrix(y_true, y_pred, model_name):\n",
    "    \"\"\"\n",
    "    绘制混淆矩阵（优化版）\n",
    "    \n",
    "    参数:\n",
    "    y_true: 真实标签\n",
    "    y_pred: 预测标签\n",
    "    model_name: 模型名称\n",
    "    \"\"\"\n",
    "    try:\n",
    "        # 计算混淆矩阵\n",
    "        cm = confusion_matrix(y_true, y_pred)\n",
    "        \n",
    "        # 创建画布\n",
    "        plt.figure(figsize=(8, 6))\n",
    "        \n",
    "        # 绘制热力图（使用Seaborn的改进版）\n",
    "        sns.heatmap(\n",
    "            cm,\n",
    "            annot=True,\n",
    "            fmt='d',\n",
    "            cmap='Blues',\n",
    "            xticklabels=['负面', '正面'],\n",
    "            yticklabels=['负面', '正面'],\n",
    "            linewidths=0.5,  # 添加边框间距\n",
    "            annot_kws={'fontsize': 12}  # 调整文本大小\n",
    "        )\n",
    "        \n",
    "        # 设置标签和标题\n",
    "        plt.xlabel('预测标签', fontsize=14)\n",
    "        plt.ylabel('真实标签', fontsize=14)\n",
    "        plt.title(f'{model_name} 混淆矩阵', fontsize=16, pad=20)  # 增加标题间距\n",
    "        \n",
    "\n",
    "        # 显示图像\n",
    "        plt.show()\n",
    "        \n",
    "    except Exception as e:\n",
    "        print(f\"绘制混淆矩阵时出错 ({model_name}): {str(e)}\")\n",
    "        plt.close()  # 关闭异常图像避免占用内存\n",
    "\n",
    "# 为每个模型绘制混淆矩阵（添加异常处理）\n",
    "for model_name, y_pred in predictions.items():\n",
    "    try:\n",
    "        plot_confusion_matrix(test_labels, y_pred, model_name)\n",
    "    except Exception as e:\n",
    "        print(f\"处理 {model_name} 时出错: {str(e)}\")\n",
    "        continue"
   ],
   "id": "6a896e1e28892f73",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ],
      "image/png": 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qHT58WGfOnNGdO3ccfhetWrXSwYMH9cMPP6h37966dOmSQkND5e/vb7fVTdKr7bZt26Zt27al2K9r165ZA653331Xv/zyi/r166ciRYqoQoUKqlKlipo2baqiRYs+8ndgNps1fPhwBQYGysfHRx4eHnJ1dbUujomNjVXhwoWtGeq8efPq8uXLGjlypA4cOKCoqKgU551++eWXOnv2rBYvXqwnnnhCP/74o0aOHGkdBvf29k4xc+nt7S0p+fzBpGA8SdJm3H/99ZfKly+fbFW1o30ondmnM+mtM+fOnVPr1q31zDPPPPAcANkfgSOQgm7duunw4cNatmxZsrrjx4+ra9euslgsatCggQICAlSxYkWZzWbru4wfVoECBVS7dm1t375dd+/e1alTp/Tnn38m21PRYrFIkkaOHHnfvQTTUlLgGhERoX/++UdPPPGEtm7dqoEDBypHjhxq0KCBXn31VT377LM6f/683dB1kiZNmmjcuHFav369evfubd3n8N+rgJOeb926dSpXrpxTfdu+fbu2b9+ukydP6sSJE5oxY4Zmz56tGTNmpPiu6NRYu3atNm7cqLp166pt27YyGAw6dOiQXcZx9uzZ1vdGz5w5U1u2bJG3t7cGDBiQYuY3ISFBe/fu1Z07dxQVFaXAwEDt2rVL3t7eGjlypD766CO9//77ev/99x32K6X3VCe5cuWKNQDv0aOHvvrqK7s5pJIcDoHfb6/RyMhIhYaGWoe/nRmSB/D4IHAEUtCwYUMVL15c58+fT1Y3e/Zs3blzR8uXL7db3btz5840ube/v7/27NmjnTt36siRIzIYDMnmTCYtuEhpO5hTp07p559/VpUqVVSpUqU06ZMk/fPPP9bfJ2XWpk2bpsTERK1Zs8Yu2/XvofwkSUPSwcHBOn36tDZs2KCiRYsmW6ldsmRJ7d69W+fPn08WOO7YsUOXL19WkyZNVLBgQZnNZp05c0Z58uRRy5YtrXMlT5w4oXbt2mnKlCmPFDiazWb93//9n8qVK6fWrVtby/89x1W6t0hHurcw5L333lObNm3k7u6e4nX379+vGzduyN/fXyVKlFCHDh0UHx+voKAg3b59W5IUGBhoN4SfZNOmTZo4ceJ9+50077N69eo6cuSIevTooVWrVtllHR9m9X1cXJx69+6tuLg4Va1aVWXLltXIkSPl5uZmXZQjyZoJX7Vqlfbt2yfp3sIys9msF154gQ3CgWyKwBFIgdFoVOfOne3etZvk77//lmSfabl48eID/yB3lp+fn7y8vLR27VqdPHlSvr6+yYYg69atq3z58un7779Xp06drAs3EhISNH78eB09elQLFixIk/4k+fLLLyXdG95M2mrl77//lpubm937q0+ePKk5c+Y4vE7Lli0VHBys6dOn6+zZs3rnnXeSZc6aN2+uJUuW6PPPP9cLL7xgXaV+7do1DR8+XDExMXrttdck3Qtk2rVrpzJlymj16tXWYfKSJUvKZDLZBbwPY9myZfrzzz/1wQcf2L095X6WLl36wLcPJWVbk/5S8NJLL+mll16SJGvgGBsbq+jo6GTnprTFj61bt25p8eLF8vDw0Pz58zV79mwtXbpUb7/9doqvIXRGUhY4X7586t69uwoVKqQ33nhDmzdv1nfffSc3Nze7OZdJ+1ceOnTIusI6afugAgUKEDgC2RSBI+BA69at9fHHHysyMtKuvH79+jpz5ox69uypl156SX/++ad27txpzcIl/aH/sDw9PdWkSROtWbNG0r35eym1+eijj/TOO++odevW8vPzU8GCBXXgwAGdPn1afn5+euGFFx66D7bb8dy8eVNHjx7VmTNn5ObmpmHDhlnb1a9fXxs3blTHjh1Vs2ZNhYWFac+ePdY5cil9F76+vipatKhCQkJkMBjs3siSpHLlygoICNC8efPUtGlTNWjQQCaTSZs3b9bt27cVGBho3WvQy8tLr7/+uoKDg9WyZUvVrFlTRqNRISEhunPnjtq0afPQ38Pff/+tTz/9VNWrV7e+yUX6XxAl3dvTc/ny5Xav/vt3IBwQEKC2bdtaFzzdvn1bmzZtUp48eVSrVi2H958xY4ZmzJiRqj5bLBaNGjVKN27c0DvvvKNcuXJpxIgROnLkiH7++WdNnz5dcXFxqbqmdC9bmGTIkCHW3zdr1izF/SyTtkhicQzweCFwBBzw8vJS27ZttWjRIrvygQMHys3NTd99952WLVsmHx8fderUSa+//rr8/f21Y8cODRs27KHeB5zk9ddf15o1a+Tu7p7iUKUkNWjQQCtXrtTcuXO1d+9emc1mlSxZUh988IF1Ht7Dst2Ox8PDw5pd6tatm91bQCZMmKAnnnhCW7ZssWbZ3nnnHT333HPq1q2bfvzxR7uASpI1WJwzZ45q1qzpcPHKoEGDVK5cOX355Zf6/vvvZTKZVL58eU2ePFl169a1axsUFKRKlSopODhY69evV2xsrIoWLarhw4erS5cuD/09fPjhh4qKirIL3s1ms3UKQ1BQkNauXauoqCiHr+i7dOmSduzYoSeffNIaOC5ZskR3795Vo0aNrFss2UoK7CZOnJjiW2BWr16d4msxLRaLPvjgA23ZskUVKlSw7grg5uamyZMnq02bNsqXL591isPZs2dTvIbZbNbt27dVoEABazbbNnAE8N9F4Ij/vKJFi1q3j/m3ESNGaMSIEXZlbm5uGjhwoAYOHJisvaMVzg+6z7/Vrl3bqbbPPvtssnl2jyI1fZTurb4dM2aMxowZk6zuftdJmqP5oFfjNW3a1OFWNrZcXV3VoUOHVL015UG2bNmijRs3qnbt2nr++eet5XPnztWdO3d0/Phx/frrr9a3AZUrV07Hjx+3nvvCCy8oMTFRX3zxhSRZN2+/deuWli9fblf2b9evX0+xPCoqSpMnT9bevXslyW6fTovFogEDBmjz5s3KkyePPv74Y7u/vJQpU0Zr167V008/bd2m6EH7jQYEBFgDx9RmKRMSEiQ9eFgdQPZC4Aggw/Tt21dPPfWUfvjhBxUoUMCpoDCzNGjQQD169Ei2sKZMmTIymUzq1KmTunfvroIFC1rrateuLR8fH02aNMn6Wkmj0agXX3zROtTt7e1tzdDaBqS2HM3LzJkzp/Llyydvb2916tTJbvGTwWBQ27ZtdejQIc2dOzfFOZZJAfu4ceN09+5dh8+ekJAgs9lsN483tRnHO3fuSJJiYmJSdR6ArM1gsZ2sAwDpqH79+rpx44bKlCmj8ePH69lnn83sLj2U8PDwNNkfMj3YbowOAGmNwBEAAABOcW5vCQAAAPznETgCAADAKQSOAAAAcAqBIwAAAJxC4AgAAACnEDgCAADAKQSOAAAAcAqBIwAAAJxC4AgAAACnEDgCAADAKQSOAAAAcAqBIwAAAJxC4AgAAACnEDgCAADAKQSOAAAAcAqBIwAAAJxC4AgAAACnEDgCAADAKQSOAAAAcAqBIwAAAJxC4AgAAACnEDgCAADAKQSOAAAAcIprZncAAAAgK/Gs2j/drn336Jx0u3ZG+E8Ejun5HwCAzHP36BzFxGd2LwCkB4//RISS/fCvBQAAwJaBmXyO8M0AAADAKWQcAQAAbBkMmd2DLIuMIwAAAJxCxhEAAMAWcxwdInAEAACwxVC1Q4TUAAAAcAoZRwAAAFsMVTvENwMAAACnkHEEAACwxRxHh8g4AgAAwClkHAEAAGwxx9EhvhkAAAA4hYwjAACALeY4OkTgCAAAYIuhaof4ZgAAAOAUMo4AAAC2GKp2iIwjAAAAnELGEQAAwBZzHB3imwEAAIBTyDgCAADYYo6jQ2QcAQAA4BQyjgAAALaY4+gQgSMAAIAtAkeH+GYAAADgFDKOAAAAtowsjnGEjCMAAACcQsYRAADAFnMcHeKbAQAAgFPIOAIAANhiA3CHyDgCAADAKWQcAQAAbDHH0SECRwAAAFsMVTtESA0AAACnkHEEAACwxVC1Q3wzAAAAcAoZRwAAAFvMcXSIjCMAAACcQsYRAADAFnMcHeKbAQAAgFPIOAIAANhijqNDBI4AAAC2GKp2iG8GAAAATiHjCAAAYIuhaofIOAIAAMApZBwBAABsMcfRIb4ZAAAAOIXAEQAAwJbBmH5HKm3dulWNGjVShQoV1LZtW4WFhUmSzp49q9atW8vX11eTJ0+WxWKxnvOwdc4gcAQAAMiCLl68qJEjR2rIkCEKCQlR4cKFNWrUKJnNZgUEBKhixYpavXq1wsLCFBwcLEkPXecsAkcAAABbBkP6HakQFhamQYMGqVmzZsqfP7/at2+vEydOKCQkRFFRUQoMDFTx4sU1ePBgffvtt5L00HXOYnEMAACArXRcHGM2m2U2m+3KTCaTTCZTsrYNGjSw+/z777+rRIkSOn36tKpUqSJPT09JUtmyZa1D2A9b5ywyjgAAABlk/vz5ql69ut0xf/78B55nNpu1aNEidejQQVFRUSpatKi1zmAwyGg06tatWw9d5ywyjgAAALbScQPwPn36qHv37nZlKWUb/23WrFnKkSOH3njjDc2aNSvZOe7u7oqJiZGLi8tD1Xl7ezvVfwJHAACADOJoWPp+9u7dq5UrV2rVqlVyc3OTt7e3zp07Z9cmOjr6keqcxVA1AACArSy0Hc+lS5c0dOhQffDBB3rmmWckSZUqVdKxY8esbcLDw2U2m+Xt7f3Qdc4icAQAAMiCYmJi1KdPH/n5+alRo0aKjo5WdHS0atSoocjISK1du1aStGDBAtWpU0cuLi7y9fV9qDpnGSyp3fkxG/Ks2j+zuwAgHdw9Okcx8ZndCwDpwSMTJ9N5tvo83a59N7iH0223bt2qt99+O1n5tm3bdPr0aQ0ZMkReXl5KSEjQsmXLVLp0aet5D1PnDAJHANkWgSPw+CJwfLCrV68qNDRU1apVU758+dKk7kFYHAMAAGDDkI6rqtOSj4+PfHx80rTuQQgcAQAAbGSXwDEzsDgGAAAATiHjCAAAYIuEo0NkHAEAAOAUMo4AAAA2mOPoGBlHAAAAOIWMIwAAgA0yjo6RcQQAAIBTyDgCAADYIOPoGIEjAACADQJHxxiqBgAAgFPIOAIAANgi4egQGUcAAAA4hYwjAACADeY4OkbGEQAAAE4h4wgAAGCDjKNjZBwBAADgFDKOAAAANsg4OkbGEQAAAE4h4wgAAGCDjKNjBI4AAAC2iBsdYqgaAAAATiHjCAAAYIOhasfIOAIAAMApZBwBAABskHF0jIwjAAAAnELGEQAAwAYZR8fIOAIAAMApZBwBAABskXB0iMARAADABkPVjjFUDQAAAKeQcQQAALBBxtExMo4AAABwChlHAAAAG2QcHSPjCAAAAKeQcQQAALBBxtExMo4AAABwChlHAAAAWyQcHSJwBAAAsMFQtWMMVQMAAMApZBwBAABskHF0jIwjAAAAnELGEQAAwAYZR8fIOAIAAMApmR44xsXFqUePHpndDQAAgHsM6Xhkc5k+VO3m5qYTJ05YP/fu3VsRERFydU3eNYvFori4OAUHB2dkFwEAAKAsEDhK9nMJzp8/r0mTJiVrY7FYNGzYME2bNi0juwYAAP5jmOPoWJYLHA0Gg2rWrJliO09PT4d1AAAAaYHA0bEsEThaLBa739erVy/Fdjdv3sygHiEz5fP20sz32uqVehXl4e6mA8d+V6+xS3XxrwhJUp83XtDgbn4qXMBbV/+5rVGz1unrTT9Zz69Upohmjmir6hVLKCExUas3H9WAj1Yq1hwvSfJwd9OkwS3Vpkl15fby0Inzf6rvuOU6diY8U54X+C+7efOGJk4I0u6QnYqNNeu5qlU1/sOJKlKkqJo2bqg//7yc4nkbNm9TkSJFdeb0aX00YZzOnD6tnDm99HLTVzVw0BC5mUwZ/CRA+rhx44Zat26tJUuWqGjRogoODlZgYGCydhMnTlSrVq0UFBSkZcuWWcuLFy+uLVu2SJLOnj2rwMBAXbx4UW3atNHw4cNTHSRnicDx3xnHXbt2yWKxqGXLllq7dq38/f21du1aNW3aNBN7iYyybPJbKlvSRxMXbFJ8QoLe69VUK6b1VL2OU9ThtZqaPryNFq3Zp0PHf1eX12vp8wlddOq3vxR69rIK5M2p7z59W5f+itCQKd+qZNEnNLirnyJuRStw5hpJ0qz33lCLhlX0yfLtuvZPpIa91USrZ/dRxRbjrMElgIwxbPBA/f77b+rT9225uLhowbz/09BBA/TVqmB9/On/yWw227X/Yf132vzjRuXPX0B3797VoHffVr16L2jAwMG68McfmjFtsoxGo4YMG5FJT4THQVbJOEZERKhv3766fPl/f4F67bXX5OfnZ/18584d+fv7y9fXV5J08uRJLViwQFWrVpUkGY331kGbzWYFBASoXr16mjlzpiZMmKDg4GC1bt06VX3KEoHjzZs31aVLF7388suKjY2Vi4uLtS7p97ZleHw1eL6sfCs9peptPrRmGCPvxGre+x1Vsmh+je37qkbPXqdZS7dJktZsPaqL2ybp9YZVFHr2svq1f0l/Xrspvx6zZI67FwQWLpBHrRpXVeDMNSpVvIA6vlZTDbrN0E8nL0iSwi79rY3zB+j5yiUV8tO5zHlw4D/owP59Cj1+XKvXrVeRIkUlSTm8vDRu7GiFh19S6TJl7drHmc0asm2revXpK3d3dx08sF83b9zQiJGj5eLiouo1fPXn5XBt2riBwBFZltlsTvYXIpPJJFMKWfLBgwerWbNm+uWXXxy2XbFihZo0aaJixYopPj5eZ8+eVY0aNeTl5WV3rZCQEEVFRSkwMFCenp4aPHiwxo0blz0Dx1y5cqlLly4KCQlRZGSkZsyYIYvFouvXr2vGjBnWX2/dupXZXUU6O3Lygup3nmoNGiUp4ma0JMnVxah2gz/Tr79dsdZF3zUrNi5eJrd7/ykv/HaPFgXvtQaNkhRxK1purvf+4vHXtVuq03GKQs9eTnb9pGsAyBgVn62k5Su/sQaNkpQnT15JUnxcXLL2a4K/lcFgUKvWbSXdG8KT7Kc7xcXFyeTunp7dxn9AemYc58+frzlz5tiV9e/fX++8806ytkFBQSpWrJg++uijFK8VGxurJUuWaNWqVZKkM2fOyGKxyN/fX1evXpWvr6+CgoJUuHBhnT59WlWqVJGnp6ckqWzZsgoLC0t1/7PEn5QuLi7y8/OTn5+f9uzZo2LFikm6F2nb/prSFj14vNyOitHtqCt2ZU3qVtC1iEidv/i33R8QkvR85ZLKmzuH9v/ymyTp8rWbya7pV7u8Dhy7V38nxmwXNEpSk3oVZY6L108n/ki7BwHwQLly5VKuXLnsyvbsDlHefPlUvMRTduUWi0VLFn+hDp26WOcvVq1aTQkJCZo752P16NVbYefPa93aYHXu+lZGPQKQan369FH37t3tylLKNkqyxkOOrF+/XlWqVFHRovf+8hUWFqbSpUtr9OjRyps3rz788EONHTtWCxcuVFRUlLWddC84NhqNunXrlry9vZ3uf5aIxGyDAYPBoLZt26bYbuHChRnVJWQRTxV5Qp1bPK8J8zcmCxolaUzfV/Xrb39p056TKZ7/ZjNflXv6SQWMW55ivXdOTw3o1EBL1h3Qzci7adp3AKkTfumSvlsbrL5vv2Odl5Vkd8guXb9+Xa+3bGUt83nySY35YLxGvTdcn382X5LU5OVX1L1HzwztNx5D6TjF0dGw9MNYuXKlXaayRYsWatGihfXzmDFj5Ofnp6ioKLm4uCS7r7u7u2JiYrJf4GibErZYLOrYsWOK7a5cuaKOHTtq+fKUgwA8XgwGgxaM66TLV2/q0xU7ktV39a+tRrXKqcXbn6YYVBbMl0tThrbSqk0/6eDx31O8x4wRbeViNOrD+RvSvP8AnJeYmKixowPl8+ST6tCpS7L6VStXqGmzV5U7d25r2Z9/XtbUSR/Jv1Vr1a1XX7+FhemLRQv10YRxGj12XEZ2H8hwFy5c0MWLF1WnTh2HbXLnzq3ExERdu3ZN3t7eOnfOfh5/dHS03NzcUnXfTA8c4+LiFBMTY/384YcfKj4+PsVh6cTERMXHs+r1v2JI98aqXeVpNe45S3dj7Oc7PVO8oKYMaaX5q0K0Zd+vKZ6/YHwnmc3xGjhxVYr1b7xSXR1eq6nOIxbpyvXbad5/AM774vPP9MvRn7Xoy2XWOVhJbtyI0P59ezV3vv2o05LFX+i5qtU0Luh/878KFy6iMaPeU6cu3fTUUyUzpO94/GSVVdX3s3HjRr300kt2gd/EiRNVpUoVNWvWTJIUGhoqo9GoQoUKqVKlSvr222+tbcPDw2U2m1OVbZSyQODo6uqqRYsWWT/Xrl07E3uDrOJF3zJ6v++r+uDT9TpwzD5bmMPDpK+m9dRv4dc1YnrKr58c2bupGtYsp6Z9PtaN23eS1Zd7+kl9OqaDFnyzW99u/jldngGAcw4e2K9PP5mt/gMG6rmq1ZLVb92yWTlz5lT1Gr525Rf++F1Pl3rGrqx02XsrscMvXSRwxEPLDoHj7t271apVK7uy8uXLa9asWSpQoIDi4+MVFBSkli1bytPTU76+voqMjLRuc7hgwQLVqVMn1bvWZHrgaDAYrHsNOeP9999X69atVbly5XTsFTJTuaef1IqpPbRx9wlNX7zVrs5oNGjZlLdUuKC36nackuK+i+1eqaFRfZrqvRlrtPdo8hVjPk/k0tpP+ur0b39p2NTV6fYcAB4s7Px5DR30rl548SV179ErxTbbtmxR3fovJBuJypM3r06dPGFXFrLz3rSWggV90qfDQBYQExOjY8eOafz48Xbl/v7+CgsLU79+/eTl5SU/Pz+7BcZBQUEaMmSIpkyZooSEBLuNwp1lsKQ0OSyTNW/eXPHx8daIPyEhQX369FG5cuXUvn17bd68WT4+zv9PwbNq//TqKtKYq6tRh74OVMF8udVh2EJF3Ym11v1x+R/1bFNP4/o31/tz1mvr/v8NUUdGx+jchWsqVbyADq0MVOi5yxo8+Ru7a4eevay4+AR9/3/9VbdqKXV57wu7Vdh//X1Lf/3Nlk/Zyd2jcxTD7JVsKy4uTm+0el3//POPps2cbbfvXJGiRZUnT17Fmc2qV9tXQ0cEqu0bb9qdv/nHjRo2eKDq1quvcuUr6MIff2jH9q2qWq26Pl+8NKMfB2nMIxNTW88M3Zhu1z4/LXNfZnL16lWFhoaqWrVqypcvX6rPz/SMY5JTp06pQoUKkqRbt25p2rRpdvUlSpTQRx99pPbt26cqaET2UrFUYZV/upAk6cfP3rWr6zV2qVo3uZedHte/ucb1b26tC/npnF7uNVvN6j+rHJ4mPV+5pPYuH253ftlmYxUZHaNGtcpJklbN7G1XP2HeBhbJABno/Plz+u23e6MCvd7qalc3fsJEvd6ylY4d+0UxMTF67rnkI1NNXm6qhKmJ+vKLz/XV8qUyGo164aUGGjl6bIb0H8iOfHx8HimOyjIZx0aNGqlixYoaPny4unbtqm3bttnVX7x4Ua+//rq2bt2qJ554IlXXJuMIPJ7IOAKPr8zMOJYetindrn1u6ivpdu2MYHxwk4xhsVhUrFgxtWzZUrGxscnqT506pTZt2qQ6aAQAAEDayDJD1QaDQcOGDVP79u3VqVMnSdLw4cMVGxurOnXq6JVXXtErr2TvKB0AAGR92WBRdabJ9Iyj2WzW7dv/20OvaNGi1qXhzZs3V40aNbRv3z41bdpUu3fvzqxuAgAA/OdlesZx06ZN+vjjjxUZGZmsrn79+qpfv746d+6sn376Sf3799f69etVoECBTOgpAAD4L8gO+zhmlkzPODZv3lzdu3dXXFycVq1K+Q0fklSjRg21bduW91UDAIB0ZTCk35HdZXrG0WAwqGPHjqpRo4b69u2rmzdvKiIiQuXLl7dr165dO/Xo0UPt2rXTiBEjZDRmeswLAADwn5LpgWOSsmXLavHixWrfvr06d+6sbt26yWAwyGKxKCEhQSaTSd7e3vL09NSJEyd4cwwAAEgXRuNjkBpMJ1kqbVe8eHF99NFH2rlzp/Lly6e8efMqX758KlCggPUl3DVr1pTZbM7kngIAAPz3ZJmMY5IXX3xRzz33nMP6iRMnZlxnAADAf87jMBcxvWSpjGOSpOwiAAAAso4sl3EEAADITGzH41iWzDgCAAAg6yHjCAAAYIOEo2MEjgAAADYYqnaMoWoAAAA4hYwjAACADTKOjpFxBAAAgFPIOAIAANgg4egYGUcAAAA4hYwjAACADeY4OkbGEQAAAE4h4wgAAGCDhKNjBI4AAAA2GKp2jKFqAAAAOIWMIwAAgA0Sjo6RcQQAAIBTyDgCAADYYI6jY2QcAQAA4BQyjgAAADZIODpGxhEAAABOIeMIAABggzmOjpFxBAAAgFPIOAIAANgg4egYgSMAAIANhqodY6gaAAAATiHjCAAAYIOEo2NkHAEAAOAUMo4AAAA2mOPoGBlHAAAAOIWMIwAAgA0Sjo6RcQQAAIBTyDgCAADYYI6jYwSOAAAANggcHWOoGgAAAE4h4wgAAGCDhKNjZBwBAADgFAJHAAAAGwaDId2O1Lpx44YaNmyo8PBwa1lQUJDKli1rPRo3bmytO3v2rFq3bi1fX19NnjxZFovFqTpnETgCAABkQREREQoICNDly5ftyk+ePKkFCxbo8OHDOnz4sNasWSNJMpvNCggIUMWKFbV69WqFhYUpODj4gXWpQeAIAABgw2BIvyM1Bg8erGbNmtmVxcfH6+zZs6pRo4Zy586t3LlzK2fOnJKkkJAQRUVFKTAwUMWLF9fgwYP17bffPrAuNQgcAQAAMojZbFZUVJTdYTabU2wbFBSkrl272pWdOXNGFotF/v7+qly5snr06KE///xTknT69GlVqVJFnp6ekqSyZcsqLCzsgXWpQeAIAABgIz3nOM6fP1/Vq1e3O+bPn59iP4oVK5asLCwsTKVLl9b06dP1ww8/yM3NTWPHjpUkRUVFqWjRonbPYTQadevWrfvWpQbb8QAAANhIz+14+vTpo+7du9uVmUwmp89v0aKFWrRoYf08ZswY+fn5KSoqSi4uLsmu5e7urpiYmPvWeXt7O31/AkcAAIAMYjKZUhUoPkju3LmVmJioa9euydvbW+fOnbOrj46Olpub233rUoOhagAAABtGgyHdjkc1ceJEbdiwwfo5NDRURqNRhQoVUqVKlXTs2DFrXXh4uMxms7y9ve9bl6rv5pGfAAAAABmifPnymjVrlg4fPqz9+/crKChILVu2lKenp3x9fRUZGam1a9dKkhYsWKA6derIxcXlvnWpwVA1AACAjaz8ykF/f3+FhYWpX79+8vLykp+fnwYPHixJcnV1VVBQkIYMGaIpU6YoISFBy5Yte2BdahgsD7NteDbjWbV/ZncBQDq4e3SOYuIzuxcA0oNHJqa2mnx6IN2uvfntWul27SRXr15VaGioqlWrpnz58jld5wwyjgAAADYe5tWAWYmPj498fHxSXecM5jgCAADAKWQcAQAAbBizd8IxXRE4AgAA2MjuQ9XpiaFqAAAAOIWMIwAAgA0Sjo6RcQQAAIBTyDgCAADYMIiUoyNkHAEAAOAUMo4AAAA22I7HMTKOAAAAcAoZRwAAABvs4+gYgSMAAIAN4kbHGKoGAACAU8g4AgAA2DCScnSIjCMAAACcQsYRAADABglHx9I04xgZGZmWlwMAAEAWkqrA8ebNm+rVq5fDAPGzzz7TgAED0qRjAAAAmcFgMKTbkd2lKnA0Go3as2ePw/qwsDAlJCSkWLdmzRq9/PLLqesdAAAAsoxUBY6urvemRMbGxmrVqlWKj4+31sXExGjfvn1q1KhRiufevXtXf//99yN0FQAAIP0ZDOl3ZHepXhxjsVjUuHFjxcbGKl++fPLz85Mkbd++XYmJiWrcuLEkafHixerYsaPc3Nzu3cjV1fp7AACArIrteBxzOuN4+vRpa8Zw/PjxOnjwoDVotFgsmj9/vgoXLqyFCxfq559/1qRJk9SwYUN9/PHHCg8Pl4uLi4xGdv8BAADIrpyO5KZNmyZ/f38ZDAY1atRIuXLlstatW7dO58+f1wsvvKAvvvhCZcqU0cSJE1W5cmV98cUXatKkiebMmZMuDwAAAJCWDOl4ZHdOB47dunVT2bJlZbFY1L9/f0VEREiS/vzzT02ZMkVdunRRuXLl5O7urpw5c6ply5b69NNPtXv3bo0ZM0YeHh7p9hAAAABIf04HjvXq1dPKlSs1c+ZMhYaGqmfPnvr777/Vr18/Pfnkkxo0aJBcXFysC2iS5MyZU+3bt1evXr3SvPMAAABpje14HEvV4pgrV67IYrHoyy+/1O+//642bdrIaDRq5cqVMplM1nYxMTHavn27Dhw4oJ9++kmtWrXSE088keadBwAAQMZJVeB46tQpjRs3TgcPHpSPj4/MZrPi4uJ04cIF+fj4WNtdv35dgwcPVunSpVWnTh1VqVJF4eHhad55AACAtGbM/onBdJOqwNHDw8OaWbx7967Wrl2rt956S3379tXKlSuVmJioxMREFS1aVCEhISpYsKD1XAJHAACA7C1V++MYDAZFRkbq5ZdfVocOHVSgQAF9/vnn8vDw0ODBgxUVFSWz2SxJ1qDx1q1bCgsLS/ueAwAApAPmODrmdMZx5syZun37thITE1WrVi3VqFFDcXFxevLJJzVp0iT16tVLy5YtU968eSVJ586d08KFC7VhwwZVrFhR7dq1S7eHAAAASCuPQXyXbpwOHLdu3aqwsDB5e3tr6NChdvs41q9fX35+fjp48KB27NghSfruu+8UGhqqDz/8UE2aNNHGjRvTvvcAAADIME4PVQcHB2vgwIEym81q06aNzp07Z1ffu3dvRUZGasWKFZKkd955R99//71atGghDw8PWSwWu3dbAwAAZEUMVTvmdODo7u6ugIAAffXVV4qJiVGnTp107Ngxa33lypVVqlQprVy5UpJkMpnsXjEYHx9vnf8IAACA7CfVL48uV66cFi1aJLPZnGzRS6NGjXTt2rUUF8MUK1ZMr7766sP3FAAAIAMYDel3ZHcGi8VieZgTf/31V5UvX96uLDQ0VO7u7ipTpkyadC6teFbtn9ldAJAO7h6doxhmwACPJY9UbRiYtrp9dTzdrr24feV0u3ZGeOh/Lf8OGiWpUqVKj9QZAACAzPY4zEVML6keqv63c+fOadWqVSnWnTlzRu+99x6LYgAAAB4DqQocr1y5onnz5ikyMtJa9ssvv2jChAl27aKiojR+/Hi1bt1a69atU3BwcNr0FgAAIJ0Z0vHI7lIVON68eVOzZ8/W3bt3rWVubm7y8PCwfv7+++/1yiuvaMWKFapQoYJmzZqltm3bpl2PAQAA0pHRYEi3I7tL1RxHNzc3SZKXl5e1zGg0ysXFxfp57969kqTJkyfr9ddfT4s+AgAAIAtIVeCYNFnUNnD8t1GjRkmScubM+QjdAgAAyByPQWIw3aTJYveoqCj17+94yxuj0ahSpUopICBA7u7uaXFLAAAAZLA0CRwtFotu3brlsP7mzZvasmWLXFxc7htgAgAAZDa243EsTQLHXLlyaenSpQ7rLRaL6tevr/379xM4AgAAZFOpChyTXjKTmJho9x5qW99++63OnDmjbt26qUiRIpLuRe7lypWTr6/vI3YXAAAgfZFwdCxVgePdu3etw9J58+aVdC+ITExMtLbZvn27tm/frhUrVujFF19U586dVbt2bS1cuDBtew4AAIAMlarAMVeuXGrZsqVdWVxcnO7cuWP9PHfuXJ07d04bN27U119/rR07dqhKlSpauXJl2vQYAAAgHT0O+y2ml1RtAF6iRAlNnDhRefPm1caNG/XGG2/oueee06hRoxQfH6/169erWbNm8vLy0oABA7Rjxw4NGDBAzz77bHr1HwAAABnkod9VHR0drQsXLqh06dJ66aWX9Oabb2r48OEymUy6ePGiJMlkMqlv374aPXp0mnUYAAAgPRkM6Xek1o0bN9SwYUOFh4dby7Zu3apGjRqpQoUKatu2rcLCwqx1QUFBKlu2rPVo3Lixte7s2bNq3bq1fH19NXnyZOvaldRweqj6l19+0e3bt+Xu7i6j0ahLly7JYDDop59+UkJCgqKjo9WxY0e9/PLLkqTDhw9bz42Pj1dcXJzi4uLUqFGjVHcSAAAgo2SV7XgiIiLUt29fXb582Vp28eJFjRw5Uh988IFq1qypoKAgjRo1yjol8OTJk1qwYIGqVq0qSdbFzGazWQEBAapXr55mzpypCRMmKDg4WK1bt05Vn5wOHOfMmaM9e/bYlRkMBnXu3Nn6+Y8//tDy5csdRrAGg0G//vprqjoIAADwuDCbzTKbzXZlJpNJJpMpWdvBgwerWbNm+uWXX6xlYWFhGjRokJo1ayZJat++vXr27CnpXqLu7NmzqlGjRrK3/IWEhCgqKkqBgYHy9PTU4MGDNW7cuPQLHD/44AOZzWaZTCYZDAZt3rxZn3zyiV5++WUdO3ZMv/32m1xcXNSkSRN16dJFBQsWtJ6bkJCguLg4xcfHp6pzaeXu0TmZcl8A6c8jTXajBYD/eeh5fE6YP3++5syxj0v69++vd955J1nboKAgFStWTB999JG1rEGDBnZtfv/9d5UoUUKSdObMGVksFvn7++vq1avy9fVVUFCQChcurNOnT6tKlSry9PSUJJUtW9ZuiNtZTv8vt2jRonafc+XKJQ8PD02cOFGS9Ntvv2nt2rVasWKFtm7dqn79+qlPnz4O93vMSJExiQ9uBCDbyeVhlGetEZndDQDp4O6ByZndhXTRp08fde/e3a4spWyjJBUrVuy+1zKbzVq0aJG6desm6V42snTp0ho9erTy5s2rDz/8UGPHjtXChQsVFRVlF8sZDAYZjUbdunVL3t7eTvf/of+ubjab7V4z+PTTT2vw4MF66623NG3aNM2ePVvh4eH68MMPH/YWAAAAGS495zg6GpZ+GLNmzVKOHDn0xhtvSJJatGihFi1aWOvHjBkjPz8/RUVFycXFJdl93d3dFRMTkzGBY4cOHdShQ4dk5Xny5NGECRNUp04d1a1b92EvDwAAAAf27t2rlStXatWqVXJzc0uxTe7cuZWYmKhr167J29tb586ds6uPjo52eK4j6TaO3KxZs1RFsAAAAFmB0ZB+R1q4dOmShg4dqg8++EDPPPOMtXzixInasGGD9XNoaKiMRqMKFSqkSpUq6dixY9a68PBwmc3mVMdqaR44xsTEKDAwUMePH7crP3PmTFrfCgAA4D8lJiZGffr0kZ+fnxo1aqTo6GhFR0fLYrGofPnymjVrlg4fPqz9+/crKChILVu2lKenp3x9fRUZGam1a9dKkhYsWKA6derIxcUlVfdP9VD1b7/9phYtWsjDw0OxsbH68ccfVbhwYWu9xWLRmjVr9NJLL6ly5cqS7gWNrVu3fqhl3wAAABkprTKD6WHPnj0KCwtTWFiYVq1aZS3ftm2b/P39FRYWpn79+snLy0t+fn4aPHiwJMnV1VVBQUEaMmSIpkyZooSEBC1btizV93+oOY7x8fGqVKmSJMnDw8OuLmnipe0EzKlTp8pgMKhKlSoPczsAAIAMk1U2AE9iO2rr5+d331HcIUOGaMiQISnW+fn5afPmzQoNDVW1atWUL1++VPfloQJHg8GgL774IsW6pJRn0mTLAwcOaM+ePQoICLAbhwcAAEDG8vHxkY+Pz0Ofn+5b506fPl3FixdXv3790vtWAAAAjywrD1VntnTdnTspHfrOO++k2Z5FAAAAyByPlHEcOnSoLly4oOLFi6to0aJ66qmnVKxYMevcgDNnzqhEiRJq3rx5mnQWAAAgvWWxKY5ZSqoDx5MnT1p//9tvv+n27du6cuWKtm/frrt370q6Nwfyq6++0tixY627mQMAACB7czpw/Oabb7Rw4UJdvHjRWhYcHGzX5saNGwoPD1fbtm0VEhKiFi1a6LPPPnukSZgAAAAZyUjK0SGn5ziGhYXp5s2batSokV15bGysOnfurPPnzytv3rzWbXomTpyo0qVLq3v37nbBJgAAALInpwPHXr16aefOnRo0aJBd+Zdffqljx44l23k8T548mjdvnnLnzq3hw4enTW8BAADSmTEdj+zO6Wd44okn5OnpabcpZkREhBYsWKA+ffqoZMmS2rVrl6Kioqz1OXPm1IgRI3Ts2DHt3LkzTTsOAACQHgyG9Duyu0cKfjds2CB3d3f17NlTV69e1aBBg/TFF1/YBZcvv/yyChcurCVLljxyZwEAAJB5Hilw7NSpkxYvXix3d3dNmzZN3t7eeuutt2SxWKxtDAaDmjZtqoMHDyoiIuKROwwAAJCejAZDuh3Z3UMHjmfOnNHp06dVpEgRhYWF6YcfftCwYcPk5eUlSXbBY9OmTZWQkKBt27Y9eo8BAACQKR4qcLRYLPL391fLli31888/q3jx4ho5cqSaNWumhIQESbL+Kknly5eXh4eHjh8/nja9BgAASCfMcXQs1RuA58uXT8OGDZOHh4fMZrOeeuopubm5qVOnTpLubc9j+6skGY1G9enTR7169UqjbgMAACCjpTpwzJMnj3r06OGw3t3dXcuXL9fTTz9tV963b9/U9w4AACCDGR+DzGB6SfMthVxcXFS9enVFRkbqiy++UExMTFrfAgAAAJkgVYFjRESENmzY4FTbCxcuaMqUKdYh6w0bNigxMTH1PQQAAMhArKp2LFWB419//aXZs2c71dbV9d4ouMlk0rVr1zRixAhNnz499T0EAADIQCyOcSxVcxzd3d3l7u4uSdq2bVuyN8nYOnXqlKR7C2OWL1+uhIQEvfrqq4/YXQAAAGSWVAWORqPR+k7qt99+22HQKN3bsiepfvXq1WrcuLEqVKjwCF0FAABIfyyOceyRFsdMmTJFkydPliRNnDhRFotF48eP15NPPqkRI0ZIko4eParr16+zqhoAACCbS/V2PLaee+456xtiKleuLIPBoEqVKsnT01PlypWTxWLR8ePH9eyzz6pcuXJp0mEAAID0ZBApR0ceKXB8EIPBoG7duqlRo0bpeRsAAABkAKcDx82bN+vkyZMym82KiIhI1U1KlSqV6o4BAABkBuY4OuZ04HjixAktWLBABoNBdevWve/CGAAAADx+nF4c079/f61YsUKFChXS+PHjU3WTq1evprpjAAAAmcFoSL8ju3M6cDSZTMqTJ4+8vb3Vtm1b66KYlNjWbd++Xd26dbtvewAAgKzCYDCk25Hdpdm7qm2/DNvf58yZU7///rs2b96cVrcCAABAJnikVdU9evSQdC/D2Lt3b1ksFvXv31/Xrl3TqFGjJEk1a9bUCy+8oGXLlunll19+9B4DAACko8dhSDm9PHTgWKtWLXl4eMhkMln3aCxfvrwkqUKFCoqIiNBff/0lSerbt686dOigS5cuqVixYmnQbQAAAGS0VAWOiYmJMpvNkqTFixfft+2+ffvUo0cPJSYmqmrVqipdurRWr16tgQMHPmxfAQAA0t1jMBUx3aQqcIyJiVFMTIxTbePi4iRJCQkJkqTmzZsrb968qeweAAAAsopUBY5ly5bV119/7VTbhIQE5cyZU7GxsfLy8lL37t3l6pquL6oBAAB4ZEZSjg6lKpJzc3NT/vz5nWrbsGFDHT58+H83ImgEAADI1ojmAAAAbLCq2jECRwAAABuMVDuWZhuAAwAA4PFGxhEAAMCGUaQcHSHjCAAAAKeQcQQAALDBHEfHyDgCAADAKWQcAQAAbLAdj2NkHAEAAOAUMo4AAAA2eOWgYwSOAAAANogbHWOoGgAAAE4h4wgAAGCDoWrHyDgCAADAKQSOAAAANgyG9DtS68aNG2rYsKHCw8OtZWfPnlXr1q3l6+uryZMny2KxPHKdswgcAQAAsqCIiAgFBATo8uXL1jKz2ayAgABVrFhRq1evVlhYmIKDgx+pLjUIHAEAAGwY0/FIjcGDB6tZs2Z2ZSEhIYqKilJgYKCKFy+uwYMH69tvv32kutQgcAQAAMggZrNZUVFRdofZbE6xbVBQkLp27WpXdvr0aVWpUkWenp6SpLJlyyosLOyR6lKDwBEAAMCGwWBIt2P+/PmqXr263TF//vwU+1GsWLFkZVFRUSpatKhdX41Go27duvXQdanBdjwAAAA20nMznj59+qh79+52ZSaTyenzXVxckrV3d3dXTEzMQ9d5e3s7fX8yjgAAABnEZDIpZ86cdkdqAkdvb29FRETYlUVHR8vNze2h61KDwBEAAMCG0WBIt+NRVapUSceOHbN+Dg8Pl9lslre390PXpeq7eeQnAAAAQIbw9fVVZGSk1q5dK0lasGCB6tSpIxcXl4euSw2D5WF2f8xmImMSM7sLANJBLg+jPGuNyOxuAEgHdw9MzrR7Lz8S/uBGD6lj9aIPbvQvZcuW1bZt26yLW7Zu3aohQ4bIy8tLCQkJWrZsmUqXLv1Idc4icASQbRE4Ao8vAsf7u3r1qkJDQ1WtWjXly5cvTeqcwapqAAAAG2kwFTHd+fj4yMfHJ03rnMEcRwAAADiFjCMAAIANQ3ZIOWYSAkcAAAAbDMc6xncDAAAAp5BxBAAAsMFQtWNkHAEAAOAUMo4AAAA2yDc6RsYRAAAATiHjCAAAYIM5jo6RcQQAAIBTyDgCAADYIKvmGN8NAAAAnELGEQAAwAZzHB0jcAQAALBB2OgYQ9UAAABwChlHAAAAG4xUO0bGEQAAAE4h4wgAAGDDyCxHh8g4AgAAwClkHAEAAGwwx9ExMo4AAABwChlHAAAAGwbmODpE4AgAAGCDoWrHGKoGAACAU8g4AgAA2GA7HsfIOAIAAMApZBwBAABsMMfRMTKOAAAAcAoZRwAAABtkHB0j4wgAAACnkHEEAACwwQbgjhE4AgAA2DASNzrEUDUAAACcQsYRAADABkPVjpFxBAAAgFPIOAIAANhgOx7HyDgCAADAKZkeOMbFxalHjx6Z3Q0AAABJ9+Y4ptc/2V2mD1W7ubnpxIkT1s+9e/dWRESEXF2Td81isSguLk7BwcEZ2UUAAAAoCwSOkmSwmUxw/vx5TZo0KVkbi8WiYcOGadq0aRnZNQAA8B/DPo6OZbnA0WAwqGbNmim28/T0dFgHAACQFh6HIeX0kiUCR4vFYvf7evXqpdju5s2bGdQjZDW/njqp79au1o0bN1SuXAW1a99RnjlySJKio6O1csVSnTt7Rnnz5lOrNm+odJmykqT5/zdHn837NMVr9gp4W3369s+wZwAg5cudQzOHvq5X6paTh8lVB0IvqFfQN7r41w1rGy9Pk3Yv6q93p67V7p9/S/E6Pvly6ujKIZqyeIdmrQixqytV7An1blVbRQt668yFv/Xp13v0z6076fpcwH9Fpi+OkZJnHHft2qWdO3cqb9682rVrl/XXwoULZ2IvkVl+PnJYPbt11N27d1WsWHF9tWKJ+vftJYvFIrPZrF7dO2vXjm0q+XQp/RZ2Xl07vqFzZ89Iklq1fkNLVnxjd3wy9zO5uLioXPkKmfxkwH/Psg87qt5zJTXx820aNWejKpZ6Uis+6mStd3Ux6vP326l8SZ/7XmfKwOa6FhGlOV/vsSt/plh+7f1igIo/mUen/7imto2raNfCt+Xp7pYuz4PHk8GQfkd2lyUyjjdv3lSXLl308ssvKzY2Vi4uLta6pN/bluG/ZfJHQer6Vi/1DnhbklS3/ovq2a2jzp45rUMH98tsjtXiZWtkMpmUkJCgN1o113drV2vI8JEqULCgChQsaHe9T2ZNV9lyFfTiSw0z43GA/6wGvs/It2IxVe8405phjLwTq3mj2qhEoby68k+kVk/rJm8vj/tep1HN0nqjyXN6bcBCxSck2tVNfvc1rdt5Qn0mfCNJWrT2kM6vHym/50trfcip9Hkw4D8kSwSOuXLlUpcuXRQSEqLIyEjNmDFDFotF169f14wZM6y/3rp1K7O7igxmNpvVsXM3NWzU2FpWoMC9QDDObFaFis/quarVZDKZJN37C8YTTzwhs9mc4vVuRERo1coVmjrj4/TvPAA7R06Fq/5bc+yGpSNuRUuSTG4u8nkilyJu3VG7EUt0fUdQitdwN7lq9jB/fbfzhLYdOpesftXmX7T3l9+tn6/diFJcfILcTVnijztkE49BYjDdZImfJBcXF/n5+cnPz0979uxRsWLFJEmDBw+2+zWlLXrweDOZTGrh38r6OTExUUu//FyFCxdRufIV5OpmP/x09OefdOzYL+rRu2+K1/t21VcqWqyYatWpm679BpDc7egY3Y6OsStrUrucrkVE6fylfyRJXcasuO81hnVpoFLF8iv0/F/6YtybCj33lz5dtVex5nhJ0tebf7Fr/277+roTE5dikAkg9bJEJGa7OMZgMKht27Yptlu4cGFGdQlZUPC3q7RsyRe6ffuWFny+xC5ovPDH75r80Xgd+emw3h4wSM/XqpPs/Pj4eAV/u0q9+76dkd0G4MBThfOp86vVNWHhFrs/BxwpkNdLAzu+IEkqUtBbcfEJeqNxFbVsUEmNAv5P5rgEa9vmL1TQyB5+KvdUQb02YKFu3L6bbs+Bx4/xcZiMmE6yROBouzjGYrGoY8eOKba7cuWKOnbsqOXLl2dU15CFPF3qGdXwfV7ff7dGq7/5WsPeG2Wty53bWzVq1tLly5e1bs1qNXu1ufIXsJ/buHP7Vt25e0dNmzXP6K4D+BeDwaAFo9vq8rVb+vTrvU6d06FpNXl5mjRs5nrrophm9cpr9bRu6tSsuhatO2Rt+9vlCO06EqaSRZ7QO+3ra3/oBSUmPjg4BbKS4OBgBQYGJiufOHGiTp48qWXLllnLihcvri1btkiSzp49q8DAQF28eFFt2rTR8OHD7WKtR5HpgWNcXJxiYv43dPHhhx8qPj4+xWHpxMRExcfHZ2T3kIU8V7WanqtaTfVfeFGDBvRT45eb6rmq1SRJefPl01s9++jNDp30ZuvXNW/uJxr9vv0cqY0bvtcLLzaQh6dnZnQfgI0hnV9U7col1LjvfN2NjXPqnGeKFVBkdKwWBO+3lm3Y86v+un5bVcra77pxMuyK3vv4By394YgOLX1XbzSuopU//pKWj4DHWFbJN7722mvy8/Ozfr5z5478/f3l6+urVatWacGCBapataokyWi8t1GO2WxWQECA6tWrp5kzZ2rChAkKDg5W69at06RPmR44urq6atGiRdbPtWvXzsTeIKtJSEjQX3/9qaJFi1nL6tZ/US4uLvrj9zAVL15CJpNJOXPlkiTlyOGl6r7P6/ff7Pd+i4qK0v69u/XR5OkZ2n8Ayb1YvZTe791EH8z/UQdCLzh93p0Ys/78+5bdkLQkxZrjZTbfKytRKK8uXrlpHfo+GXZFF/66obJPFUx2PcChLBI5mkwm6+JPSVqxYoWaNGmiQoUK6ezZs6pRo4a8vLzszgkJCVFUVJQCAwPl6empwYMHa9y4cWkWOGb6Po4Gg0EVK1bU9u3b7crj4pL/DTQ6Olrt2rXLqK4hC7jy11/yf7WJToQet5ZdunhBCQkJKlS4iN4bNlBzPp5hd86FC7+rcOEidmV7du+SxWJRrTopby4PIGOUe6qgVnzUSRv3ntb0pbtSde6RU5dU7Mk8ypPrf6MGJQrlVdGC3jp88qJyeLjpyIrBatWwkrXeO6eHCubLpQs2K7mBzGQ2mxUVFWV3ONoJxFZsbKyWLFmi3r1768yZM7JYLPL391flypXVo0cP/fnnn5Kk06dPq0qVKvL8/6NrZcuWVVhYWJr1P9MDR+nelzFkyBC7srp169oNYUv3gsyzZ89mZNeQyYoULaoGDf00Ysi72vjDeu3fu0ejA4epXPkKql7dVx06d9Pqb77Wpx/P1E+HD2nmtMn69eQJvdG+g911Dh3Yp3LlK8jD4/77wwFIP64uRq2Y2EkWWTTn6z2qVq6I9ciXO8cDz1+784QuX7ulVZO7qHGtMmrxYkV9M7Wr/vjrhtbuPKE7MXH6fO1BfTKipXr4P6+XapTSkqAOun4zSmt3nMiAJ8TjwpCO/8yfP1/Vq1e3O+bPn//APq1fv15VqlRR0aJFFRYWptKlS2v69On64Ycf5ObmprFjx0q6N8JWtGjR/z2LwSCj0ZhmWxpm+lC1dC8V++85jUajMdkf8q6urmk2uRPZx/tBE/XJrGmaMXWSEi2Jqv/CS3p30DC5urnppQaNNG7CJH3x+QJ9tXypnildWp/M/UyVKj9nd42fDh1UA5u9IAFkvIqlnrS+EebHuX3s6noFrdKyH47c93xzXIKa9J2vCW831Wdj35CXh0kHQi/o3alrrcPXo+ZsUExsnALfaiQvT5NCjoTplbc/081IVlUja+jTp4+6d+9uV2Y7HO3IypUr9c4770iSWrRooRYtWljrxowZIz8/P0VFRcnFxSXZ9dzd3RUTEyNvb+9H7n+WCBwNBkOygJAAEUly5sypwNEfKHD0BynWN3uthZq91iLFuiTfbdyaDj0DkBrHzv4pz1ojnGrrqN2VfyLVc/wqh+fFJyTq/Xk/6v15Pz5UHwEpfV8N+O95i864cOGCLl68qDp1km81J0m5c+dWYmKirl27Jm9vb507Z79vaXR0tNzc0ua1m1liqBoAAAAp27hxo1566SVr8Ddx4kRt2LDBWh8aGiqj0ahChQqpUqVKOnbsmLUuPDxcZrM5TbKNUhbJOEr3ouEuXbpYP0dGRtp9lu5txwMAAJCestqY5+7du9Wq1f/eola+fHnNmjVLBQoUUHx8vIKCgtSyZUt5enrK19dXkZGRWrt2rfz9/bVgwQLVqVNHLi4uadKXLBM4mkwmu42/U9oEPC4uzjr5EwAA4HEXExOjY8eOafz48dYyf39/hYWFqV+/fvLy8pKfn5/d65mDgoI0ZMgQTZkyRQkJCXYbhT8qg8WZ9zylM7PZrHr16unQoUMPbFerVi39/PPPqbp+ZAyZSuBxlMvD6PScOQDZy90DkzPt3od/T5sVyCnxLZk2Q8YPcvXqVYWGhqpatWrKly9fml03y2QcbSXt4fjvtGpCQkJKzQEAANKMIcsNVqeej4+PfHx80vy6WTJwXLhwoWbPnp1sZbXFYlGOHA/e6wsAAABpL0sEjnFxcXa7pr/55pt67bXXrO9dTBIbG6s2bdpkdPcAAMB/CDsCOpYlAkd3d3f93//9n/Vz3rx5lTdv3mTtoqKiFB8fn5FdAwAAwP+XJQJHV1dX1a5d+4HtPD09tWqV441fAQAAHhUJR8ey1QbgLi4uKleuXGZ3AwAA4D8pS2QcAQAAsgxSjg5lq4wjAAAAMg8ZRwAAABuPwz6O6YXAEQAAwAbb8TjGUDUAAACcQsYRAADABglHx8g4AgAAwClkHAEAAGyRcnSIjCMAAACcQsYRAADABtvxOEbGEQAAAE4h4wgAAGCDfRwdI3AEAACwQdzoGEPVAAAAcAoZRwAAAFukHB0i4wgAAACnkHEEAACwwXY8jpFxBAAAgFPIOAIAANhgOx7HyDgCAADAKWQcAQAAbJBwdIyMIwAAAJxCxhEAAMAWKUeHCBwBAABssB2PYwxVAwAAwClkHAEAAGywHY9jZBwBAADgFDKOAAAANkg4OkbGEQAAAE4h4wgAAGCLlKNDZBwBAADgFDKOAAAANtjH0TECRwAAABtsx+MYQ9UAAABwChlHAAAAGyQcHSPjCAAAAKeQcQQAALBFytEhMo4AAABwChlHAAAAG2zH4xgZRwAAADiFjCMAAIAN9nF0jMARAADABnGjYwxVAwAAwCkEjgAAALYM6XikQlBQkMqWLWs9GjduLEk6e/asWrduLV9fX02ePFkWi8V6zv3q0gKBIwAAQBZ08uRJLViwQIcPH9bhw4e1Zs0amc1mBQQEqGLFilq9erXCwsIUHBwsSfetSysEjgAAADYM6fiP2WxWVFSU3WE2m5P1IT4+XmfPnlWNGjWUO3du5c6dWzlz5lRISIiioqIUGBio4sWLa/Dgwfr2228l6b51aYXAEQAAIIPMnz9f1atXtzvmz5+frN2ZM2dksVjk7++vypUrq0ePHvrzzz91+vRpValSRZ6enpKksmXLKiwsTJLuW5dWWFUNAABgIz234+nTp4+6d+9uV2YymZK1CwsLU+nSpTV69GjlzZtXH374ocaOHatnnnlGRYsWtemrQUajUbdu3VJUVJTDOm9v7zTpP4EjAABABjGZTCkGiv/WokULtWjRwvp5zJgx8vPz09NPP53sfHd3d8XExMjFxcVhXVoFjgxVAwAA2Mgii6rt5M6dW4mJicqfP78iIiLs6qKjo+Xm5iZvb2+HdWmFwBEAAMCGwZB+h7MmTpyoDRs2WD+HhobKaDSqbNmyOnbsmLU8PDxcZrNZ3t7eqlSpksO6tELgCAAAkMWUL19es2bN0uHDh7V//34FBQWpZcuWqlu3riIjI7V27VpJ0oIFC1SnTh25uLjI19fXYV1aMVjSemfILCgyJjGzuwAgHeTyMMqz1ojM7gaAdHD3wORMu3f4jeTb46SVonkfPL8xyfTp07Vy5Up5eXnJz89PgwcPVo4cObR161YNGTJEXl5eSkhI0LJly1S6dGlJum9dWiBwBJBtETgCjy8Cx/u7evWqQkNDVa1aNeXLl8/pukfFqmoAAAAb6bkdT1rx8fGRj49PquseFXMcAQAA4BQyjgAAADayQcIx05BxBAAAgFPIOAIAANjIDnMcMwuBIwAAgA0Dg9UOMVQNAAAAp5BxBAAAsEXC0SEyjgAAAHAKGUcAAAAbJBwdI+MIAAAAp5BxBAAAsMF2PI6RcQQAAIBTyDgCAADYYB9HxwgcAQAAbBE3OsRQNQAAAJxCxhEAAMAGCUfHyDgCAADAKWQcAQAAbLAdj2NkHAEAAOAUMo4AAAA22I7HMTKOAAAAcAoZRwAAABvMcXSMjCMAAACcQuAIAAAApzBUDQAAYIOhasfIOAIAAMApZBwBAABssB2PY2QcAQAA4BQyjgAAADaY4+gYGUcAAAA4hYwjAACADRKOjhE4AgAA2CJydIihagAAADiFjCMAAIANtuNxjIwjAAAAnELGEQAAwAbb8ThGxhEAAABOIeMIAABgg4SjY2QcAQAA4BQyjgAAALZIOTpExhEAAABOIeMIAABgg30cHSNwBAAAsMF2PI4xVA0AAACnGCwWiyWzOwEAAICsj4wjAAAAnELgCAAAAKcQOAIAAMApBI4AAABwCoEjAAAAnELgCAAAAKcQOAIAAMApBI4AAABwCoEjso3ExEStW7dOsbGx921jNpudup7FYnG6LYDMx883kPl4VzWyja+++kqLFi3SSy+9JHd39xTb/PLLL3rrrbfs6hMTExUZGSlvb2+7thaLRc8++6wWLVqUrv0GkLK4uDjFxsbKzc3NYZukANDDw0MnTpzg5xvIZLxyENnCmTNn9Oabb8rFxUW5c+e2q/v444/17LPPOjx3y5YtWrJkiZYuXZre3QSQCvv371dAQIBd4BgfHy+z2awcOXJYy+Li4jR9+nT5+fkluwY/30DGIuOILO/ixYvq27evhg8frvbt21vLp0yZogsXLtw3aJSkkJAQNWnSJL27CSCVateurWPHjtmVbdq0ScuXL3c6EOTnG8hYBI7I0nbt2qXAwEB16dLFLmgMDw/XqlWrtGbNmmTn7N+/X6NGjbJ+/vvvv5U7d2598cUXdu2efvppLVy4MP06DyDN8fMNZC4CR2RZN2/e1LRp09SqVSvNnz9fq1atstbFx8fLZDKpa9euiouLk7e3t77//ntJ94a1fHx89NVXX+mbb77RhQsXNHToUMXHx+vFF1/U3r17dfDgQU2ePDmzHg3A/xcREaGVK1eqZ8+eMplMD2zPzzeQuQgckWXlyZNH3333nSIiIvTZZ5/p6NGjKbY7ePCgxo4da/3s6urcf9bOtgOQfqKjozV79mz17NnTWvbzzz/r+eeft35OSEjQvn37ZDKZ+PkGMhk/WcjSDAaDDAaDXVlISIjWr1+vqVOn2rVz5Ntvv9XOnTvTq4sAHkHSz67R+L/d4apVq2ad42ixWHT37l2H2Uh+voGMxT6OyHb++OOP++7l+G9t2rTR999/r7Vr16ZfpwCkC4PBYLfC+t/4+QYyFhlHZHnx8fF2n3/66SfVrl3briwxMdHh+WQkgMcXP99AxiLjiCzPZDKpXbt2ku4FjYcOHdIrr7yijRs36sKFCypRooR69+7t8HwyEsDji59vIGORcUSWlydPHvXq1UvTp0/XqlWrNGXKFOXNm1dHjhxRYGCgXnjhBfXp08faPjExUSdPnlTjxo0VFRWl+Ph4/fjjj5KkGzduqHHjxoqJidGTTz6ZWY8E/OfFx8crJibG+lpAs9mshIQExcfHKzEx0W46Snx8vCwWi3LmzMnPN5DJCByRZVksFgUGBurQoUO6c+eOmjdvru+++04+Pj6SpNGjR6tPnz5auHCh2rdvL39/f40fP14JCQmqWLGivvrqK4fXPnjwoCZOnJhRjwLgX86fP6+2bdvK1dVVOXLkUN26de3qa9WqZf19YmKiypQpo2+++YafbyCT8cpBZGmHDh2SyWTSs88+e9/tNa5cuaKrV6+qSpUqGdg7AAD+WwgcAQAA4BQWxwAAAMApBI4AAABwCoEjAAAAnELgCAAAAKcQOAIAAMApBI4AAABwCoEjgAx1/fp13b17N9XnRUREaNmyZQoLC0tWd+nSJfXo0UM///xzWnRRMTExatasmaZPn55i/e7du9WjRw+dOnUqTe4HANkFb44B8EB3796VxWKRyWRKthF7RESEwsPD5e7unuIm7XFxcTKbzXr22WdlMBgUEBCgqKgoTZ8+XRUrVtT+/ft17dq1ZOcVKlRINWvWtH6+efOmgoKCNHPmTJUqVcqurdls1p49e9SlSxdr2R9//KHIyEh5enrKYDDYtbdYLIqLi1OePHlUqFChZPdevHixwsLC5Ovrq3379slisSgmJkbly5dX4cKF9emnn+r06dPKkSOHoqOjJcn6qry8efM68Y0CQPZE4AjggcaNG6c1a9YkKy9durR69OihMWPGyGQyyc3NTbdv37a+Rk66F9SZzWYdOnRIXl5eGjp0qIYMGaL27dtr/vz5Wrx4sX766ScVKFDAet2//vpLjRo1sgsc3dzcJEkmkylZP5LKbOvmzZuXYp9tvfvuu+rXr59d2enTpzV//nx5e3srODhYa9euVa5cuRQbG6sPPvhAx44d09GjRyVJL7/8st259erV0+eff37fewJAdkbgCOCBhg8frgEDBsjV1VWurq767LPP9PXXX2vw4MFq2LChWrZsaW37/PPPq3379ho4cGCK16pVq5bWrFmjTz/9VNWrV9eKFSvUuHFjTZo0ydqmc+fO1kDx+eefV5s2bdS+fXuH/UvKKBqN/5t9M3z4cA0fPly5c+e2y4QmJibqzp07io6OloeHh911wsLC1KdPHz3xxBNavXq1pk+frgMHDujHH3+UwWDQyZMn1bVrV9WqVUuffvqptm/frjFjxmjz5s3JrgUAjyMCRwAPlC9fPv36669auHChnn32Wa1cuVKLFy9O9m7wGzdu6ObNmw98Z3jBggU1btw4Sf8L+q5du6b169erQYMGduUeHh5yd3dP8TorVqyQm5uboqKiJEl79+7VxYsX5eXlpWbNmqV4jtFoVM6cOZUzZ0678lOnTqljx45ycXHRsmXL5O3trRo1amj79u0KDw+Xp6en/vjjDxUtWlTTp09Xzpw59dJLLyk2NlY7duzQm2++ed9nBoDHAYEjAKcUKVJEiYmJmjRpkgICAuyCw/fff19r1qyR0WiUi4uLBg0aZA384uPjNWXKFDVt2lR37tzRsmXL9NZbbyWbD3n37l1NmTIl2RxBFxeXZHMUk0yYMEG5c+dWbGysJOnrr7+WxWJR8eLFHQaOjlSoUEGjRo1SqVKlVK5cOUlS48aN1axZM/3zzz9q3ry5XF1d9e233yp//vySpNy5c+uTTz7Riy++mKp7AUB2ReAI4IHu3LmjHDlyaObMmapSpUqybJ3RaFTJkiW1bt06RURE6Nq1aypevLhiYmJUu3Ztubi4SJJ27dqlGTNmaPfu3frkk0+UJ08e6zU8PT0l3QvGnHXixAkZjUa99957WrNmjT7++GM9//zzOnv2rL755hu5ubk5DDotFovMZrOee+45lSlTRpLUpk0buzaenp66cuWKevToodjYWJlMJvXu3Vtz585VkSJFJN0LLgHgv4LAEcADvfDCC4qMjLQrGzVqlCSpX79+dkPJISEhGjFihNasWaMnn3xS0v8WtjRt2lQuLi4aMmSI+vTpo6+//jrZvVIzVzBpTuOxY8ck3Zu/+Ndff+nIkSP68MMP5enpaW0TFRUlg8EgLy8vSVJCQoJiYmI0evRoa+D4b7t379Z7772nO3fuaO7cuSpZsqT69u2r1q1b67333lOLFi3s5lUCwOOOwBHAAy1dulRubm6Kj4/X66+/rjFjxqh8+fLq0KGDnnvuOR04cMDaNmnYuGTJkinu19ikSRPNmjXLGsAlsVgskuRwPqMjYWFh+u233yTdW/3t5eWlr776Ktlims6dOytXrlyaO3fuA695/PhxLViwQFu2bNEzzzyjRYsWqWzZspKklStXaubMmRo5cqQWLVqkgIAANWjQwJoxBYDHGYEjgAcqX768JFn3LMyfP7/+/vtvGQyGFANHLy8veXp6Otzou1GjRtZAMenX69evS5J1G5+k8gdZt26dXFxclJCQoLp162rZsmXatGmTWrRokapnPH78uLZu3aqtW7cqLCxMOXPm1FNPPaV27drp1KlTOn36tLVtmTJl1KlTJ2t21dXVVbVr11apUqXUs2dPeXt7p+reAJBdEDgCeCgNGzbU5s2bkwVJ0dHR1sUjjsTFxalTp04aPXq0zGazvLy8rJuAJ10vKXN5Pzdu3NDy5cvVuHFjbdq0SU2aNNFPP/2kuXPnqnnz5nr77bfVoEEDtW3b1npOVFSUJkyYoLp166p58+bWcnd3d33++ecqWLCgBg4cqI4dO+r111/X9OnTZTKZ7Iak4+PjFRsbq+7du6tnz57asmWLtm3bpt9//52gEcBjjcARwEMxmUwqXry4pHvzBZNcvnzZOrcxiW29JO3cuVO//PKLwsLCZDAY5OrqqqNHj8rNzU2FChWSt7e3deg3MTFR8fHxKfZh+vTpio2NVZcuXbRp0yZJ0nvvvadixYrp3Llz2rZtW7I3w1gsFu3Zs0e7d+/Wiy++aF2MU7ZsWa1bt06lSpWyLqjZsWOH9bxLly7piSeesGZEb9++rXPnzil37txq06ZNsoU1APA4YlY3gEdmNputvz9+/LhKlizpsF6SFi1apKeeekrNmzfXvHnzNGHCBIWEhKhSpUqKi4vTnDlzNHHiREn3spNxcXHJ7vnDDz/om2++UUBAgHx8fKzltWvXVtGiRbVp0yYZjUZ169bN7rxcuXJp6NChun79umbMmGFX98wzz8hgMCg2NlbTpk3T2bNnJUlXr16Vn5+fXSC5bds2dejQQYsXL3b+iwKAbI7AEcAj8/X1VfPmzXXp0iWdPXtWvr6+iouLk5eXl3UhTZL9+/fr559/Vt++fa3b9ISGhurXX39V9erV1aJFCy1atMjafvDgwSlueVO3bl21atVKAQEByepiY2O1cuVKvfjiiypWrFiyen9/f1WuXFmrVq2ym7uYxNXVVdu2bdNnn30mSfLx8VGZMmW0f/9+a5utW7eqQIEC6tixYyq+KQDI3ggcATyyV199VT179tTHH38sDw8PPf/882rVqpUOHDigTp06WTOQCQkJmjRpkooXL243v3D69OnKlSuXevbsqUqVKmnq1Knau3evJKl169aqWrVqsnvmyZNHEydOTLaRuHQvo/nPP/+oU6dODvvco0cPJSQk2AWpSVxcXBQQEKCNGzfqxo0bku69+vCnn36SdG+e5N69e9W6desU350NAI8rAkcATouJiZGkFDfVXrJkib777jsNHTpUnp6eKlGihPr27atvvvnG2ub//u//dPr0aXXr1s2abVy1apX279+vfv36KU+ePJowYYKKFy+u4cOHKyIiwnrurVu3HPYraQ5l0q///POPKleurHr16km6N6/x5s2b1ntK97YFGjdunCZMmJDiNZs0aaIKFSron3/+sX5u1aqVLBaL1q9fr5iYGOY1AvjPYXEMgAe6evWqAgMDrYGc7ZzCyMhITZs2TStXrlSnTp3UsWNHGQwGzZkzR1OnTtXo0aP1zz//KCAgQE899ZQKFy6sli1bSpL27duncePGqWLFitbsoKenp8aOHat33nlHp0+fVp06dTRgwADt3r1bklS4cOFk/UtaPJM0F3L06NFKTEyUdC+zePr0aV2/fl0NGza0nmM0GpO9X3rfvn26dOmSXF1dZTAY9Oabb+r48eM6fvy4pHvbEK1Zs0ZffvmlfHx8dPDgQe3bt0/x8fHy9PRUq1atHv3LBoAsjMARwAP5+Pjo8uXLiouLU69evazvqb548aLatm2rmJgYjRo1Sl26dLE7b9iwYZKkOXPmqFGjRnrttdfk5+dnfTtM+fLl1alTJ3Xt2tVuyLdu3brauHGjNUBt2LChcuTIoaZNm6pChQrJ+pcUMNouoknaPqdGjRq6c+eO3njjDfXu3fu+z7l+/Xp9//33MplMKQ6B/9vUqVOtq77z589P4AjgsWewOLvLLgCk4PDhw8qfP3+yldS2fv31V7sFMgCA7InAEQAAAE5hcQwAAACcQuAIAAAApxA4AgAAwCkEjgAAAHAKgSMAAACcQuAIAAAApxA4AgAAwCn/D3cPeBbcBG+2AAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 48
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-06T02:24:15.413964Z",
     "start_time": "2025-06-06T02:24:13.405151Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def compare_models(results):\n",
    "    \"\"\"比较不同模型的性能\"\"\"\n",
    "    # 设置图表\n",
    "    plt.figure(figsize=(15, 10))\n",
    "    \n",
    "    # 1. 训练时间比较\n",
    "    plt.subplot(2, 2, 1)\n",
    "    sns.barplot(x='Train Time(s)', y='Model', data=results)\n",
    "    plt.title('模型训练时间比较')\n",
    "    \n",
    "    # 2. 准确率比较\n",
    "    plt.subplot(2, 2, 2)\n",
    "    sns.barplot(x='Accuracy', y='Model', data=results)\n",
    "    plt.title('模型准确率比较')\n",
    "    \n",
    "    # 3. 综合性能比较\n",
    "    metrics = ['Accuracy', 'Precision', 'Recall', 'F1-Score']\n",
    "    results_melted = results.melt(id_vars='Model', value_vars=metrics, var_name='Metric', value_name='Score')\n",
    "    \n",
    "    plt.subplot(2, 2, 3)\n",
    "    sns.barplot(x='Score', y='Model', hue='Metric', data=results_melted)\n",
    "    plt.title('模型综合性能比较')\n",
    "    plt.legend(loc='lower right')\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    \n",
    "    print(\"\\n模型性能比较结果:\")\n",
    "    print(results.sort_values(by='Accuracy', ascending=False))\n",
    "\n",
    "# 执行模型比较\n",
    "compare_models(results)"
   ],
   "id": "c61d25fa13a6416f",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x1000 with 3 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "模型性能比较结果:\n",
      "                 Model  Train Time(s)  Accuracy  Precision  Recall  F1-Score\n",
      "0  Logistic Regression           7.99    0.8986   0.898870  0.8986  0.898583\n",
      "1  SVM (Probabilistic)           6.39    0.8954   0.895469  0.8954  0.895395\n",
      "5          Naive Bayes           5.30    0.8770   0.877210  0.8770  0.876983\n",
      "3        Random Forest          56.16    0.8558   0.856041  0.8558  0.855776\n",
      "4                  KNN          20.49    0.7758   0.777221  0.7758  0.775512\n",
      "2        Decision Tree          30.66    0.7198   0.719884  0.7198  0.719773\n"
     ]
    }
   ],
   "execution_count": 49
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-06T02:24:19.691166Z",
     "start_time": "2025-06-06T02:24:19.340925Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "def plot_roc_curves(y_test, pred_probs):\n",
    "    \"\"\"绘制所有模型的ROC曲线\"\"\"\n",
    "    plt.figure(figsize=(10, 8))\n",
    "    \n",
    "    for model_name, y_pred_prob in pred_probs.items():\n",
    "        if y_pred_prob is not None:\n",
    "            fpr, tpr, _ = roc_curve(y_test, y_pred_prob)\n",
    "            roc_auc = auc(fpr, tpr)\n",
    "            plt.plot(fpr, tpr, lw=2, label=f'{model_name} (AUC = {roc_auc:.3f})')\n",
    "    \n",
    "    plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('假正例率')\n",
    "    plt.ylabel('真正例率')\n",
    "    plt.title('各模型ROC曲线比较')\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.show()\n",
    "\n",
    "# 绘制ROC曲线\n",
    "plot_roc_curves(test_labels, pred_probs)"
   ],
   "id": "e77be7d87fe02882",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 50
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-06T02:55:45.907370Z",
     "start_time": "2025-06-06T02:54:35.901331Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, precision_score, mean_squared_error, recall_score, f1_score\n",
    "import time\n",
    "\n",
    "# 特征提取：这里以IMDb的TF-IDF特征为例\n",
    "# 你可以根据需要替换为其他特征\n",
    "X_train = tfidf_vectorizer_imdb.transform(train_reviews)\n",
    "X_test = tfidf_vectorizer_imdb.transform(test_reviews)\n",
    "y_train = train_labels\n",
    "y_test = test_labels\n",
    "\n",
    "# 训练逻辑回归模型并计时\n",
    "start_time = time.time()\n",
    "lr = LogisticRegression(max_iter=1000)\n",
    "lr.fit(X_train, y_train)\n",
    "train_time_lr = time.time() - start_time\n",
    "\n",
    "# 预测\n",
    "y_pred = lr.predict(X_test)\n",
    "\n",
    "# 评估指标\n",
    "accuracy_lr = accuracy_score(y_test, y_pred)\n",
    "precision_lr= precision_score(y_test, y_pred)\n",
    "mse_lr = mean_squared_error(y_test, y_pred)\n",
    "recall_lr = recall_score(y_test, y_pred)\n",
    "f1_lr= f1_score(y_test, y_pred)\n",
    "\n",
    "print(f\"准确率(Accuracy): {accuracy_lr:.4f}\")\n",
    "print(f\"精确率(Precision): {precision_lr:.4f}\")\n",
    "print(f\"均方误差(MSE): {mse_lr:.4f}\")\n",
    "print(f\"召回率(Recall): {recall_lr:.4f}\")\n",
    "print(f\"F1分数(F1 Score): {f1_lr:.4f}\")\n",
    "print(f\"训练时间(Training Time): {train_time_lr:.2f} 秒\")"
   ],
   "id": "46234a7c32058747",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "准确率(Accuracy): 0.8740\n",
      "精确率(Precision): 0.8704\n",
      "均方误差(MSE): 0.1260\n",
      "召回率(Recall): 0.8788\n",
      "F1分数(F1 Score): 0.8746\n",
      "训练时间(Training Time): 0.50 秒\n"
     ]
    }
   ],
   "execution_count": 58
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-06T02:42:45.322384Z",
     "start_time": "2025-06-06T02:32:18.903535Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.svm import SVC\n",
    "import time\n",
    "\n",
    "# 使用TF-IDF特征\n",
    "X_train = tfidf_vectorizer_imdb.transform(train_reviews)\n",
    "X_test = tfidf_vectorizer_imdb.transform(test_reviews)\n",
    "y_train = train_labels\n",
    "y_test = test_labels\n",
    "\n",
    "# 训练SVM并计时\n",
    "start_time = time.time()\n",
    "svm = SVC()\n",
    "svm.fit(X_train, y_train)\n",
    "train_time_svm = time.time() - start_time\n",
    "\n",
    "# 预测\n",
    "y_pred = svm.predict(X_test)\n",
    "\n",
    "# 手动计算评估指标\n",
    "# 准确率\n",
    "accuracy_svm = sum(y_pred == y_test) / len(y_test)\n",
    "\n",
    "# 精确率\n",
    "tp = sum((y_pred == 1) & (y_test == 1))\n",
    "fp = sum((y_pred == 1) & (y_test == 0))\n",
    "precision_svm = tp / (tp + fp) if (tp + fp) > 0 else 0\n",
    "\n",
    "# 召回率\n",
    "fn = sum((y_pred == 0) & (y_test == 1))\n",
    "recall = tp / (tp + fn) if (tp + fn) > 0 else 0\n",
    "\n",
    "# F1分数\n",
    "f1 = 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0\n",
    "\n",
    "# 均方误差\n",
    "mse = sum((y_pred - y_test) ** 2) / len(y_test)\n",
    "\n",
    "print(f\"准确率(Accuracy): {accuracy:.4f}\")\n",
    "print(f\"精确率(Precision): {precision:.4f}\")\n",
    "print(f\"均方误差(MSE): {mse:.4f}\")\n",
    "print(f\"召回率(Recall): {recall:.4f}\")\n",
    "print(f\"F1分数(F1 Score): {f1:.4f}\")\n",
    "print(f\"训练时间(Training Time): {train_time:.2f} 秒\")"
   ],
   "id": "43597bffc907ffa5",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "准确率(Accuracy): 0.8858\n",
      "精确率(Precision): 0.0000\n",
      "均方误差(MSE): 0.1142\n",
      "召回率(Recall): 0.0000\n",
      "F1分数(F1 Score): 0.0000\n",
      "训练时间(Training Time): 527.71 秒\n"
     ]
    }
   ],
   "execution_count": 53
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-06T02:44:55.548570Z",
     "start_time": "2025-06-06T02:43:20.296369Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import accuracy_score, precision_score, mean_squared_error, recall_score, f1_score\n",
    "import time\n",
    "\n",
    "# 使用TF-IDF特征\n",
    "X_train = tfidf_vectorizer_imdb.transform(train_reviews)\n",
    "X_test = tfidf_vectorizer_imdb.transform(test_reviews)\n",
    "y_train = train_labels\n",
    "y_test = test_labels\n",
    "\n",
    "# 训练决策树模型并计时\n",
    "start_time = time.time()\n",
    "dt = DecisionTreeClassifier()\n",
    "dt.fit(X_train, y_train)\n",
    "train_time = time.time() - start_time\n",
    "\n",
    "# 预测\n",
    "y_pred = dt.predict(X_test)\n",
    "\n",
    "# 评估指标\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "recall = recall_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "\n",
    "print(f\"准确率(Accuracy): {accuracy:.4f}\")\n",
    "print(f\"精确率(Precision): {precision:.4f}\")\n",
    "print(f\"均方误差(MSE): {mse:.4f}\")\n",
    "print(f\"召回率(Recall): {recall:.4f}\")\n",
    "print(f\"F1分数(F1 Score): {f1:.4f}\")\n",
    "print(f\"训练时间(Training Time): {train_time:.2f} 秒\")"
   ],
   "id": "92226b62f853010f",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "准确率(Accuracy): 0.7194\n",
      "精确率(Precision): 0.7211\n",
      "均方误差(MSE): 0.2806\n",
      "召回率(Recall): 0.7156\n",
      "F1分数(F1 Score): 0.7183\n",
      "训练时间(Training Time): 35.82 秒\n"
     ]
    }
   ],
   "execution_count": 54
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-06T02:47:08.066734Z",
     "start_time": "2025-06-06T02:45:14.990903Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, precision_score, mean_squared_error, recall_score, f1_score\n",
    "import time\n",
    "\n",
    "# 使用TF-IDF特征\n",
    "X_train = tfidf_vectorizer_imdb.transform(train_reviews)\n",
    "X_test = tfidf_vectorizer_imdb.transform(test_reviews)\n",
    "y_train = train_labels\n",
    "y_test = test_labels\n",
    "\n",
    "# 训练随机森林模型并计时\n",
    "start_time = time.time()\n",
    "rf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "rf.fit(X_train, y_train)\n",
    "train_time = time.time() - start_time\n",
    "\n",
    "# 预测\n",
    "y_pred = rf.predict(X_test)\n",
    "\n",
    "# 评估指标\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred)\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "recall = recall_score(y_test, y_pred)\n",
    "f1 = f1_score(y_test, y_pred)\n",
    "\n",
    "print(f\"准确率(Accuracy): {accuracy:.4f}\")\n",
    "print(f\"精确率(Precision): {precision:.4f}\")\n",
    "print(f\"均方误差(MSE): {mse:.4f}\")\n",
    "print(f\"召回率(Recall): {recall:.4f}\")\n",
    "print(f\"F1分数(F1 Score): {f1:.4f}\")\n",
    "print(f\"训练时间(Training Time): {train_time:.2f} 秒\")"
   ],
   "id": "e48e83eb8b3a62a6",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "准确率(Accuracy): 0.8442\n",
      "精确率(Precision): 0.8505\n",
      "均方误差(MSE): 0.1558\n",
      "召回率(Recall): 0.8352\n",
      "F1分数(F1 Score): 0.8428\n",
      "训练时间(Training Time): 62.89 秒\n"
     ]
    }
   ],
   "execution_count": 55
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-06T02:48:37.634187Z",
     "start_time": "2025-06-06T02:47:21.874465Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "import time\n",
    "\n",
    "# 使用TF-IDF特征\n",
    "X_train = tfidf_vectorizer_imdb.transform(train_reviews)\n",
    "X_test = tfidf_vectorizer_imdb.transform(test_reviews)\n",
    "y_train = train_labels\n",
    "y_test = test_labels\n",
    "\n",
    "# 训练K近邻模型并计时\n",
    "start_time = time.time()\n",
    "knn = KNeighborsClassifier(n_neighbors=5)\n",
    "knn.fit(X_train, y_train)\n",
    "train_time = time.time() - start_time\n",
    "\n",
    "# 预测\n",
    "y_pred = knn.predict(X_test)\n",
    "\n",
    "# 手动计算评估指标\n",
    "# 准确率\n",
    "accuracy = sum(y_pred == y_test) / len(y_test)\n",
    "\n",
    "# 精确率\n",
    "tp = sum((y_pred == 1) & (y_test == 1))\n",
    "fp = sum((y_pred == 1) & (y_test == 0))\n",
    "precision = tp / (tp + fp) if (tp + fp) > 0 else 0\n",
    "\n",
    "# 召回率\n",
    "fn = sum((y_pred == 0) & (y_test == 1))\n",
    "recall = tp / (tp + fn) if (tp + fn) > 0 else 0\n",
    "\n",
    "# F1分数\n",
    "f1 = 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0\n",
    "\n",
    "# 均方误差\n",
    "mse = sum((y_pred - y_test) ** 2) / len(y_test)\n",
    "\n",
    "print(f\"准确率(Accuracy): {accuracy:.4f}\")\n",
    "print(f\"精确率(Precision): {precision:.4f}\")\n",
    "print(f\"均方误差(MSE): {mse:.4f}\")\n",
    "print(f\"召回率(Recall): {recall:.4f}\")\n",
    "print(f\"F1分数(F1 Score): {f1:.4f}\")\n",
    "print(f\"训练时间(Training Time): {train_time:.2f} 秒\")"
   ],
   "id": "fe1e0a0f32a54ea7",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "准确率(Accuracy): 0.7048\n",
      "精确率(Precision): 0.0000\n",
      "均方误差(MSE): 0.2952\n",
      "召回率(Recall): 0.0000\n",
      "F1分数(F1 Score): 0.0000\n",
      "训练时间(Training Time): 0.01 秒\n"
     ]
    }
   ],
   "execution_count": 56
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-06T02:50:11.532518Z",
     "start_time": "2025-06-06T02:49:01.111202Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import time\n",
    "import numpy as np\n",
    "\n",
    "class NaiveBayesClassifier:\n",
    "    def fit(self, X, y):\n",
    "        self.classes = np.unique(y)\n",
    "        self.class_priors = {}\n",
    "        self.feature_probs = {}\n",
    "        X = X.toarray()\n",
    "        for c in self.classes:\n",
    "            X_c = X[y == c]\n",
    "            self.class_priors[c] = X_c.shape[0] / X.shape[0]\n",
    "            # 拉普拉斯平滑\n",
    "            self.feature_probs[c] = (X_c.sum(axis=0) + 1) / (X_c.sum() + X.shape[1])\n",
    "    def predict(self, X):\n",
    "        X = X.toarray()\n",
    "        preds = []\n",
    "        for x in X:\n",
    "            class_scores = {}\n",
    "            for c in self.classes:\n",
    "                # 计算对数概率防止下溢\n",
    "                log_prob = np.log(self.class_priors[c])\n",
    "                log_prob += (x * np.log(self.feature_probs[c])).sum()\n",
    "                class_scores[c] = log_prob\n",
    "            preds.append(max(class_scores, key=class_scores.get))\n",
    "        return np.array(preds, dtype=int)\n",
    "\n",
    "# 使用TF-IDF特征\n",
    "X_train = tfidf_vectorizer_imdb.transform(train_reviews)\n",
    "X_test = tfidf_vectorizer_imdb.transform(test_reviews)\n",
    "y_train = np.array(train_labels)\n",
    "y_test = np.array(test_labels)\n",
    "\n",
    "# 训练朴素贝叶斯模型并计时\n",
    "start_time = time.time()\n",
    "nb = NaiveBayesClassifier()\n",
    "nb.fit(X_train, y_train)\n",
    "train_time = time.time() - start_time\n",
    "\n",
    "# 预测\n",
    "y_pred = nb.predict(X_test)\n",
    "\n",
    "# 评估指标\n",
    "accuracy = np.mean(y_pred == y_test)\n",
    "tp = np.sum((y_pred == 1) & (y_test == 1))\n",
    "fp = np.sum((y_pred == 1) & (y_test == 0))\n",
    "fn = np.sum((y_pred == 0) & (y_test == 1))\n",
    "precision = tp / (tp + fp) if (tp + fp) > 0 else 0\n",
    "recall = tp / (tp + fn) if (tp + fn) > 0 else 0\n",
    "f1 = 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0\n",
    "mse = np.mean((y_pred - y_test) ** 2)\n",
    "\n",
    "print(f\"准确率(Accuracy): {accuracy:.4f}\")\n",
    "print(f\"精确率(Precision): {precision:.4f}\")\n",
    "print(f\"均方误差(MSE): {mse:.4f}\")\n",
    "print(f\"召回率(Recall): {recall:.4f}\")\n",
    "print(f\"F1分数(F1 Score): {f1:.4f}\")\n",
    "print(f\"训练时间(Training Time): {train_time:.2f} 秒\")"
   ],
   "id": "5362f0a89d92a60c",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "准确率(Accuracy): 0.8712\n",
      "精确率(Precision): 0.8763\n",
      "均方误差(MSE): 0.1288\n",
      "召回率(Recall): 0.8644\n",
      "F1分数(F1 Score): 0.8703\n",
      "训练时间(Training Time): 1.87 秒\n"
     ]
    }
   ],
   "execution_count": 57
  }
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